Dimkid CogSci 2017, 2018
Data preparation
220 parsing failures.
row # A tibble: 5 x 4 col row col expected actual expected <int> <int> <chr> <chr> actual 1 301 NA date like %m/%d/%y Jun-07 row 2 302 NA date like %m/%d/%y Jun-07 col 3 303 NA date like %m/%d/%y Jun-07 expected 4 304 NA date like %m/%d/%y Jun-07 actual 5 305 NA date like %m/%d/%y Jun-07
... ................. ... ....................................... ........ ....................................... ...... ....................................... ... ....................................... ... ....................................... ........ ....................................... ...... .......................................
See problems(...) for more details.
20 parsing failures.
row # A tibble: 5 x 4 col row col expected actual expected <int> <int> <chr> <chr> actual 1 2229 NA date like %m/%d/%y **parent but birth date as 8/23/17 row 2 2230 NA date like %m/%d/%y **parent but birth date as 8/23/17 col 3 2231 NA date like %m/%d/%y **parent but birth date as 8/23/17 expected 4 2232 NA date like %m/%d/%y **parent but birth date as 8/23/17 actual 5 2233 NA date like %m/%d/%y **parent but birth date as 8/23/17
... ................. ... ................................................................... ........ ................................................................... ...... ................................................................... ... ................................................................... ... ................................................................... ........ ................................................................... ...... ...................................................................
See problems(...) for more details.Analysis plan
For all studies we conduct exploratory factor analyses using Pearson correlations to find minimum residual solutions.
For each study, we first examine maximal unrotated and rotated solutions. To determine the maximum number of factors to extract, we use the following rule of thumb: With \(p\) observations per participant, we can extract a maximum of \(k\) factors, where \((p-k)*2 > p+k\), i.e., \(k < p/3\). Thus, with 40 mental capacity items, we can extract a maximum of 13 factors.
To determine how many factors to retain, we use the following preset retention criteria, considering the unrotated maximal solution (unless otherwise noted):
- Each factor must have an eigenvalue >1.0.
- Each factor must individually account for >5% of the total variance in the maximal model.
- After rotation, each factor must be the dominant factor (i.e., the factor with the highest factor loading) for ≥1 mental capacity item.
We then examine and interpret varimax-rotated solutions, extracting only the number of factors that meet these criteria.
Study 1 (CogSci 2017)
Study information:
- Design: 2 conditions (beetle, robot), 3-point response scale, “decide what to do” and “have intentions”
- Population: adults
- Date conducted: 2016-07-06 (MTurk)
Demographics
Joining, by = c("character", "min_age", "max_age", "median_age", "mean_age", "sd_age")
Column `character` joining factor and character vector, coercing into character vectorExploratory factor analysis
Step 1: Run maximal EFA (without and with rotation)
Factor Analysis using method = minres
Call: fa(r = d1_all, nfactors = 13, rotate = "none", fm = "minres",
cor = chosenCorType)
Standardized loadings (pattern matrix) based upon correlation matrix
MR1 MR2 MR3 MR4 MR5 MR6 MR7 MR8 MR9
angry 0.65 -0.04 -0.11 -0.03 0.05 0.22 -0.02 0.04 -0.10
beliefs 0.48 0.40 -0.16 -0.14 0.04 -0.38 0.18 0.09 0.07
calm 0.68 -0.17 0.01 -0.08 -0.08 0.04 0.08 0.10 -0.04
choices 0.37 0.34 0.36 -0.20 0.06 0.09 -0.25 -0.20 0.12
communicating 0.11 0.62 0.18 0.14 -0.18 0.30 0.11 0.18 0.15
computations -0.33 0.82 -0.07 0.14 -0.03 0.00 -0.02 0.04 0.03
conscious 0.44 0.10 0.44 -0.11 0.17 -0.11 -0.24 0.06 0.08
depressed 0.74 0.04 -0.37 0.04 -0.14 0.04 -0.17 -0.04 -0.21
depth 0.26 0.27 0.48 0.28 0.12 -0.09 -0.16 -0.09 0.04
desires 0.66 -0.17 0.10 -0.02 -0.03 -0.02 0.35 -0.48 0.13
disrespected 0.63 0.06 -0.35 0.16 0.07 0.07 -0.13 -0.03 -0.07
embarrassed 0.52 0.14 -0.40 0.19 0.48 0.18 0.11 0.11 0.12
emo_recog 0.37 0.39 -0.10 -0.10 0.01 -0.27 -0.09 0.10 0.04
fear 0.72 -0.39 0.14 0.03 -0.18 0.07 0.09 0.12 -0.03
free_will 0.31 0.30 0.32 -0.40 0.15 0.22 -0.07 -0.19 -0.05
goal 0.41 0.21 0.19 -0.11 0.07 -0.11 0.18 -0.08 0.02
guilt 0.62 0.14 -0.41 0.21 0.43 0.14 0.04 0.02 0.08
happy 0.76 0.00 -0.33 -0.08 -0.22 0.08 -0.15 -0.03 -0.08
hungry 0.55 -0.71 0.22 -0.03 0.08 -0.02 0.06 0.02 0.03
intentions 0.19 0.62 0.02 -0.16 0.01 0.07 0.26 0.00 -0.20
joy 0.76 0.01 -0.39 0.10 -0.17 -0.01 -0.08 -0.07 0.05
love 0.75 0.11 -0.28 0.09 -0.07 -0.09 -0.07 -0.14 -0.03
morality 0.31 0.50 -0.13 0.02 -0.07 -0.19 0.08 0.05 -0.04
nauseated 0.65 -0.32 0.14 0.08 -0.16 0.05 0.01 -0.06 0.14
odors 0.49 -0.35 0.37 0.05 0.15 -0.09 -0.03 0.10 0.07
pain 0.63 -0.52 0.19 -0.01 -0.04 0.11 0.11 0.19 0.05
personality 0.44 0.36 -0.19 -0.13 -0.03 -0.27 0.01 0.00 0.24
pleasure 0.69 -0.23 -0.07 0.15 -0.15 -0.08 -0.15 0.06 0.16
pride 0.68 0.18 -0.42 0.08 0.04 0.04 0.01 -0.08 -0.14
reasoning 0.34 0.44 0.31 -0.16 0.01 0.21 -0.11 0.01 0.08
recognizing 0.10 0.76 0.12 0.13 -0.21 0.15 0.11 0.11 0.15
remembering 0.14 0.66 0.16 0.10 -0.15 0.13 0.01 -0.05 0.03
safe 0.71 -0.29 0.21 -0.12 -0.03 -0.06 0.04 0.06 -0.03
seeing 0.33 0.15 0.50 0.28 0.08 -0.03 -0.07 -0.01 -0.11
self_aware 0.46 0.18 0.22 -0.30 0.09 0.00 0.12 0.13 -0.20
self_restraint 0.43 0.35 -0.05 -0.15 0.04 -0.19 -0.08 0.07 0.00
sounds 0.27 0.20 0.42 0.38 -0.06 -0.02 0.06 -0.07 -0.11
temperature 0.30 0.19 0.46 0.40 0.05 -0.22 0.04 0.00 -0.26
thoughts 0.55 0.18 0.10 -0.37 -0.01 0.04 -0.04 0.10 -0.09
tired 0.69 -0.34 0.23 0.07 -0.06 0.05 0.06 0.08 0.07
MR10 MR11 MR12 MR13 h2 u2 com
angry 0.17 0.02 -0.20 -0.09 0.58 0.42 1.8
beliefs 0.04 -0.06 -0.09 -0.04 0.64 0.36 4.0
calm 0.02 -0.01 0.05 -0.07 0.54 0.46 1.3
choices -0.05 0.17 -0.07 -0.05 0.59 0.41 6.1
communicating -0.05 -0.08 -0.01 -0.03 0.65 0.35 2.6
computations 0.00 0.03 -0.06 -0.03 0.81 0.19 1.4
conscious -0.19 -0.15 -0.21 0.05 0.63 0.37 4.6
depressed -0.05 -0.02 0.06 -0.03 0.79 0.21 2.0
depth -0.08 -0.10 -0.13 -0.02 0.53 0.47 4.1
desires -0.14 -0.08 0.02 -0.07 0.87 0.13 2.9
disrespected -0.03 -0.07 -0.02 0.02 0.59 0.41 2.0
embarrassed -0.03 0.09 0.12 0.07 0.82 0.18 4.3
emo_recog 0.08 -0.14 0.14 0.11 0.46 0.54 4.3
fear 0.13 -0.04 -0.16 0.15 0.82 0.18 2.3
free_will 0.27 0.10 0.08 0.00 0.65 0.35 6.8
goal -0.03 0.07 -0.14 0.15 0.36 0.64 4.0
guilt 0.00 -0.03 -0.03 -0.05 0.84 0.16 3.3
happy -0.04 -0.08 -0.04 -0.08 0.80 0.20 1.8
hungry 0.03 0.05 0.07 0.01 0.88 0.12 2.2
intentions 0.00 0.09 -0.01 0.18 0.60 0.40 2.3
joy 0.00 -0.03 0.09 -0.01 0.79 0.21 1.8
love -0.08 0.00 0.08 0.07 0.71 0.29 1.6
morality 0.00 0.18 -0.10 0.10 0.46 0.54 2.9
nauseated 0.03 0.03 0.04 0.05 0.61 0.39 2.0
odors 0.01 -0.03 0.11 0.05 0.56 0.44 3.4
pain -0.01 0.09 -0.01 -0.14 0.80 0.20 2.7
personality 0.20 -0.06 0.09 -0.17 0.59 0.41 5.2
pleasure -0.06 0.21 0.09 0.18 0.72 0.28 2.3
pride 0.05 -0.06 -0.16 0.01 0.74 0.26 2.2
reasoning 0.20 -0.21 0.09 0.12 0.60 0.40 5.4
recognizing -0.02 0.00 -0.02 -0.12 0.75 0.25 1.7
remembering -0.15 0.04 0.09 0.10 0.58 0.42 1.7
safe 0.09 -0.13 -0.04 0.07 0.68 0.32 1.8
seeing -0.01 0.07 0.09 -0.08 0.50 0.50 3.1
self_aware -0.28 -0.21 0.15 -0.07 0.60 0.40 5.6
self_restraint 0.02 0.17 -0.04 -0.14 0.43 0.57 3.5
sounds 0.12 -0.07 0.08 0.01 0.48 0.52 4.0
temperature 0.12 0.09 0.06 -0.09 0.65 0.35 4.8
thoughts -0.15 0.19 0.09 -0.05 0.57 0.43 2.9
tired -0.07 0.09 -0.11 -0.05 0.70 0.30 2.0
MR1 MR2 MR3 MR4 MR5 MR6 MR7 MR8 MR9 MR10
SS loadings 11.03 5.51 3.15 1.29 0.87 0.84 0.64 0.56 0.51 0.46
Proportion Var 0.28 0.14 0.08 0.03 0.02 0.02 0.02 0.01 0.01 0.01
Cumulative Var 0.28 0.41 0.49 0.52 0.55 0.57 0.58 0.60 0.61 0.62
Proportion Explained 0.42 0.21 0.12 0.05 0.03 0.03 0.02 0.02 0.02 0.02
Cumulative Proportion 0.42 0.64 0.76 0.81 0.84 0.87 0.90 0.92 0.94 0.96
MR11 MR12 MR13
SS loadings 0.43 0.38 0.32
Proportion Var 0.01 0.01 0.01
Cumulative Var 0.63 0.64 0.65
Proportion Explained 0.02 0.01 0.01
Cumulative Proportion 0.97 0.99 1.00
Mean item complexity = 3.1
Test of the hypothesis that 13 factors are sufficient.
The degrees of freedom for the null model are 780 and the objective function was 27.45 with Chi Square of 5073.12
The degrees of freedom for the model are 338 and the objective function was 2.41
The root mean square of the residuals (RMSR) is 0.02
The df corrected root mean square of the residuals is 0.03
The harmonic number of observations is 196 with the empirical chi square 93.35 with prob < 1
The total number of observations was 200 with Likelihood Chi Square = 424.01 with prob < 0.001
Tucker Lewis Index of factoring reliability = 0.951
RMSEA index = 0.046 and the 90 % confidence intervals are 0.024 0.046
BIC = -1366.82
Fit based upon off diagonal values = 1
Measures of factor score adequacy
MR1 MR2 MR3 MR4 MR5
Correlation of scores with factors 0.99 0.98 0.95 0.88 0.89
Multiple R square of scores with factors 0.98 0.95 0.91 0.77 0.79
Minimum correlation of possible factor scores 0.95 0.90 0.82 0.55 0.59
MR6 MR7 MR8 MR9 MR10
Correlation of scores with factors 0.84 0.83 0.85 0.78 0.75
Multiple R square of scores with factors 0.70 0.70 0.73 0.61 0.57
Minimum correlation of possible factor scores 0.40 0.39 0.45 0.23 0.14
MR11 MR12 MR13
Correlation of scores with factors 0.73 0.74 0.71
Multiple R square of scores with factors 0.54 0.54 0.50
Minimum correlation of possible factor scores 0.08 0.08 0.00
Factor Analysis using method = minres
Call: fa(r = d1_all, nfactors = 13, rotate = "none", fm = "minres",
cor = chosenCorType)
Standardized loadings (pattern matrix) based upon correlation matrix
MR1 MR2 MR3 MR4 MR5 MR6 MR7 MR8 MR9
angry 0.65 -0.04 -0.11 -0.03 0.05 0.22 -0.02 0.04 -0.10
beliefs 0.48 0.40 -0.16 -0.14 0.04 -0.38 0.18 0.09 0.07
calm 0.68 -0.17 0.01 -0.08 -0.08 0.04 0.08 0.10 -0.04
choices 0.37 0.34 0.36 -0.20 0.06 0.09 -0.25 -0.20 0.12
communicating 0.11 0.62 0.18 0.14 -0.18 0.30 0.11 0.18 0.15
computations -0.33 0.82 -0.07 0.14 -0.03 0.00 -0.02 0.04 0.03
conscious 0.44 0.10 0.44 -0.11 0.17 -0.11 -0.24 0.06 0.08
depressed 0.74 0.04 -0.37 0.04 -0.14 0.04 -0.17 -0.04 -0.21
depth 0.26 0.27 0.48 0.28 0.12 -0.09 -0.16 -0.09 0.04
desires 0.66 -0.17 0.10 -0.02 -0.03 -0.02 0.35 -0.48 0.13
disrespected 0.63 0.06 -0.35 0.16 0.07 0.07 -0.13 -0.03 -0.07
embarrassed 0.52 0.14 -0.40 0.19 0.48 0.18 0.11 0.11 0.12
emo_recog 0.37 0.39 -0.10 -0.10 0.01 -0.27 -0.09 0.10 0.04
fear 0.72 -0.39 0.14 0.03 -0.18 0.07 0.09 0.12 -0.03
free_will 0.31 0.30 0.32 -0.40 0.15 0.22 -0.07 -0.19 -0.05
goal 0.41 0.21 0.19 -0.11 0.07 -0.11 0.18 -0.08 0.02
guilt 0.62 0.14 -0.41 0.21 0.43 0.14 0.04 0.02 0.08
happy 0.76 0.00 -0.33 -0.08 -0.22 0.08 -0.15 -0.03 -0.08
hungry 0.55 -0.71 0.22 -0.03 0.08 -0.02 0.06 0.02 0.03
intentions 0.19 0.62 0.02 -0.16 0.01 0.07 0.26 0.00 -0.20
joy 0.76 0.01 -0.39 0.10 -0.17 -0.01 -0.08 -0.07 0.05
love 0.75 0.11 -0.28 0.09 -0.07 -0.09 -0.07 -0.14 -0.03
morality 0.31 0.50 -0.13 0.02 -0.07 -0.19 0.08 0.05 -0.04
nauseated 0.65 -0.32 0.14 0.08 -0.16 0.05 0.01 -0.06 0.14
odors 0.49 -0.35 0.37 0.05 0.15 -0.09 -0.03 0.10 0.07
pain 0.63 -0.52 0.19 -0.01 -0.04 0.11 0.11 0.19 0.05
personality 0.44 0.36 -0.19 -0.13 -0.03 -0.27 0.01 0.00 0.24
pleasure 0.69 -0.23 -0.07 0.15 -0.15 -0.08 -0.15 0.06 0.16
pride 0.68 0.18 -0.42 0.08 0.04 0.04 0.01 -0.08 -0.14
reasoning 0.34 0.44 0.31 -0.16 0.01 0.21 -0.11 0.01 0.08
recognizing 0.10 0.76 0.12 0.13 -0.21 0.15 0.11 0.11 0.15
remembering 0.14 0.66 0.16 0.10 -0.15 0.13 0.01 -0.05 0.03
safe 0.71 -0.29 0.21 -0.12 -0.03 -0.06 0.04 0.06 -0.03
seeing 0.33 0.15 0.50 0.28 0.08 -0.03 -0.07 -0.01 -0.11
self_aware 0.46 0.18 0.22 -0.30 0.09 0.00 0.12 0.13 -0.20
self_restraint 0.43 0.35 -0.05 -0.15 0.04 -0.19 -0.08 0.07 0.00
sounds 0.27 0.20 0.42 0.38 -0.06 -0.02 0.06 -0.07 -0.11
temperature 0.30 0.19 0.46 0.40 0.05 -0.22 0.04 0.00 -0.26
thoughts 0.55 0.18 0.10 -0.37 -0.01 0.04 -0.04 0.10 -0.09
tired 0.69 -0.34 0.23 0.07 -0.06 0.05 0.06 0.08 0.07
MR10 MR11 MR12 MR13 h2 u2 com
angry 0.17 0.02 -0.20 -0.09 0.58 0.42 1.8
beliefs 0.04 -0.06 -0.09 -0.04 0.64 0.36 4.0
calm 0.02 -0.01 0.05 -0.07 0.54 0.46 1.3
choices -0.05 0.17 -0.07 -0.05 0.59 0.41 6.1
communicating -0.05 -0.08 -0.01 -0.03 0.65 0.35 2.6
computations 0.00 0.03 -0.06 -0.03 0.81 0.19 1.4
conscious -0.19 -0.15 -0.21 0.05 0.63 0.37 4.6
depressed -0.05 -0.02 0.06 -0.03 0.79 0.21 2.0
depth -0.08 -0.10 -0.13 -0.02 0.53 0.47 4.1
desires -0.14 -0.08 0.02 -0.07 0.87 0.13 2.9
disrespected -0.03 -0.07 -0.02 0.02 0.59 0.41 2.0
embarrassed -0.03 0.09 0.12 0.07 0.82 0.18 4.3
emo_recog 0.08 -0.14 0.14 0.11 0.46 0.54 4.3
fear 0.13 -0.04 -0.16 0.15 0.82 0.18 2.3
free_will 0.27 0.10 0.08 0.00 0.65 0.35 6.8
goal -0.03 0.07 -0.14 0.15 0.36 0.64 4.0
guilt 0.00 -0.03 -0.03 -0.05 0.84 0.16 3.3
happy -0.04 -0.08 -0.04 -0.08 0.80 0.20 1.8
hungry 0.03 0.05 0.07 0.01 0.88 0.12 2.2
intentions 0.00 0.09 -0.01 0.18 0.60 0.40 2.3
joy 0.00 -0.03 0.09 -0.01 0.79 0.21 1.8
love -0.08 0.00 0.08 0.07 0.71 0.29 1.6
morality 0.00 0.18 -0.10 0.10 0.46 0.54 2.9
nauseated 0.03 0.03 0.04 0.05 0.61 0.39 2.0
odors 0.01 -0.03 0.11 0.05 0.56 0.44 3.4
pain -0.01 0.09 -0.01 -0.14 0.80 0.20 2.7
personality 0.20 -0.06 0.09 -0.17 0.59 0.41 5.2
pleasure -0.06 0.21 0.09 0.18 0.72 0.28 2.3
pride 0.05 -0.06 -0.16 0.01 0.74 0.26 2.2
reasoning 0.20 -0.21 0.09 0.12 0.60 0.40 5.4
recognizing -0.02 0.00 -0.02 -0.12 0.75 0.25 1.7
remembering -0.15 0.04 0.09 0.10 0.58 0.42 1.7
safe 0.09 -0.13 -0.04 0.07 0.68 0.32 1.8
seeing -0.01 0.07 0.09 -0.08 0.50 0.50 3.1
self_aware -0.28 -0.21 0.15 -0.07 0.60 0.40 5.6
self_restraint 0.02 0.17 -0.04 -0.14 0.43 0.57 3.5
sounds 0.12 -0.07 0.08 0.01 0.48 0.52 4.0
temperature 0.12 0.09 0.06 -0.09 0.65 0.35 4.8
thoughts -0.15 0.19 0.09 -0.05 0.57 0.43 2.9
tired -0.07 0.09 -0.11 -0.05 0.70 0.30 2.0
MR1 MR2 MR3 MR4 MR5 MR6 MR7 MR8 MR9 MR10
SS loadings 11.03 5.51 3.15 1.29 0.87 0.84 0.64 0.56 0.51 0.46
Proportion Var 0.28 0.14 0.08 0.03 0.02 0.02 0.02 0.01 0.01 0.01
Cumulative Var 0.28 0.41 0.49 0.52 0.55 0.57 0.58 0.60 0.61 0.62
Proportion Explained 0.42 0.21 0.12 0.05 0.03 0.03 0.02 0.02 0.02 0.02
Cumulative Proportion 0.42 0.64 0.76 0.81 0.84 0.87 0.90 0.92 0.94 0.96
MR11 MR12 MR13
SS loadings 0.43 0.38 0.32
Proportion Var 0.01 0.01 0.01
Cumulative Var 0.63 0.64 0.65
Proportion Explained 0.02 0.01 0.01
Cumulative Proportion 0.97 0.99 1.00
Mean item complexity = 3.1
Test of the hypothesis that 13 factors are sufficient.
The degrees of freedom for the null model are 780 and the objective function was 27.45 with Chi Square of 5073.12
The degrees of freedom for the model are 338 and the objective function was 2.41
The root mean square of the residuals (RMSR) is 0.02
The df corrected root mean square of the residuals is 0.03
The harmonic number of observations is 196 with the empirical chi square 93.35 with prob < 1
The total number of observations was 200 with Likelihood Chi Square = 424.01 with prob < 0.001
Tucker Lewis Index of factoring reliability = 0.951
RMSEA index = 0.046 and the 90 % confidence intervals are 0.024 0.046
BIC = -1366.82
Fit based upon off diagonal values = 1
Measures of factor score adequacy
MR1 MR2 MR3 MR4 MR5
Correlation of scores with factors 0.99 0.98 0.95 0.88 0.89
Multiple R square of scores with factors 0.98 0.95 0.91 0.77 0.79
Minimum correlation of possible factor scores 0.95 0.90 0.82 0.55 0.59
MR6 MR7 MR8 MR9 MR10
Correlation of scores with factors 0.84 0.83 0.85 0.78 0.75
Multiple R square of scores with factors 0.70 0.70 0.73 0.61 0.57
Minimum correlation of possible factor scores 0.40 0.39 0.45 0.23 0.14
MR11 MR12 MR13
Correlation of scores with factors 0.73 0.74 0.71
Multiple R square of scores with factors 0.54 0.54 0.50
Minimum correlation of possible factor scores 0.08 0.08 0.00
[1] 3[1] 3Step 2: Run EFA with rotation
Factor Analysis using method = minres
Call: fa(r = d1_all, nfactors = nfactors_d1_all, rotate = chosenRotType,
fm = "minres", cor = chosenCorType)
Standardized loadings (pattern matrix) based upon correlation matrix
MR1 MR2 MR3 h2 u2 com
angry 0.50 0.27 0.09 0.43 0.57 1.6
beliefs 0.53 -0.17 0.24 0.40 0.60 1.6
calm 0.39 0.45 0.12 0.50 0.50 2.1
choices 0.03 0.08 0.59 0.36 0.64 1.0
communicating 0.07 -0.33 0.53 0.41 0.59 1.7
computations 0.04 -0.83 0.33 0.80 0.20 1.3
conscious -0.05 0.34 0.54 0.37 0.63 1.7
depressed 0.81 0.10 -0.06 0.68 0.32 1.0
depth -0.17 0.15 0.62 0.35 0.65 1.3
desires 0.31 0.45 0.17 0.44 0.56 2.1
disrespected 0.72 0.05 -0.06 0.53 0.47 1.0
embarrassed 0.66 -0.06 -0.07 0.39 0.61 1.0
emo_recog 0.41 -0.18 0.26 0.29 0.71 2.1
fear 0.26 0.70 0.11 0.68 0.32 1.3
free_will 0.02 0.06 0.49 0.25 0.75 1.0
goal 0.15 0.12 0.41 0.25 0.75 1.4
guilt 0.75 -0.03 -0.05 0.53 0.47 1.0
happy 0.78 0.17 -0.04 0.68 0.32 1.1
hungry 0.00 0.93 -0.06 0.87 0.13 1.0
intentions 0.25 -0.38 0.43 0.41 0.59 2.6
joy 0.84 0.12 -0.09 0.73 0.27 1.1
love 0.76 0.10 0.06 0.66 0.34 1.0
morality 0.43 -0.32 0.28 0.36 0.64 2.6
nauseated 0.23 0.62 0.13 0.54 0.46 1.4
odors -0.08 0.69 0.25 0.50 0.50 1.3
pain 0.13 0.79 0.05 0.70 0.30 1.1
personality 0.52 -0.18 0.19 0.34 0.66 1.5
pleasure 0.44 0.44 0.02 0.51 0.49 2.0
pride 0.85 -0.06 -0.04 0.68 0.32 1.0
reasoning 0.07 -0.02 0.60 0.39 0.61 1.0
recognizing 0.14 -0.48 0.57 0.59 0.41 2.1
remembering 0.11 -0.37 0.56 0.48 0.52 1.8
safe 0.22 0.65 0.22 0.63 0.37 1.5
seeing -0.17 0.29 0.59 0.37 0.63 1.6
self_aware 0.15 0.18 0.40 0.27 0.73 1.7
self_restraint 0.40 -0.10 0.29 0.30 0.70 2.0
sounds -0.13 0.18 0.53 0.27 0.73 1.3
temperature -0.13 0.21 0.55 0.30 0.70 1.4
thoughts 0.31 0.16 0.34 0.33 0.67 2.4
tired 0.17 0.70 0.20 0.64 0.36 1.3
MR1 MR2 MR3
SS loadings 7.56 6.63 5.04
Proportion Var 0.19 0.17 0.13
Cumulative Var 0.19 0.35 0.48
Proportion Explained 0.39 0.34 0.26
Cumulative Proportion 0.39 0.74 1.00
With factor correlations of
MR1 MR2 MR3
MR1 1.00 0.28 0.30
MR2 0.28 1.00 -0.01
MR3 0.30 -0.01 1.00
Mean item complexity = 1.5
Test of the hypothesis that 3 factors are sufficient.
The degrees of freedom for the null model are 780 and the objective function was 27.45 with Chi Square of 5073.12
The degrees of freedom for the model are 663 and the objective function was 6.67
The root mean square of the residuals (RMSR) is 0.05
The df corrected root mean square of the residuals is 0.05
The harmonic number of observations is 196 with the empirical chi square 729.79 with prob < 0.036
The total number of observations was 200 with Likelihood Chi Square = 1219.34 with prob < 2.1e-35
Tucker Lewis Index of factoring reliability = 0.846
RMSEA index = 0.071 and the 90 % confidence intervals are 0.059 NA
BIC = -2293.44
Fit based upon off diagonal values = 0.98
Measures of factor score adequacy
MR1 MR2 MR3
Correlation of scores with factors 0.97 0.98 0.95
Multiple R square of scores with factors 0.95 0.96 0.89
Minimum correlation of possible factor scores 0.89 0.92 0.79Factor loadings table
Study 2 (CogSci 2017)
Study information:
- Design: 2 conditions (beetle, robot), 3-point response scale, “decide what to do” and “have intentions”
- Population: 7-9yo children
- Date conducted: June - December 2016
Demographics
Joining, by = c("character", "min_age", "max_age", "median_age", "mean_age", "sd_age")
Column `character` joining factor and character vector, coercing into character vectorExploratory factor analysis
Step 1: Run maximal EFA (without and with rotation)
Factor Analysis using method = minres
Call: fa(r = d2_all, nfactors = 13, rotate = "none", fm = "minres",
cor = chosenCorType)
Standardized loadings (pattern matrix) based upon correlation matrix
MR1 MR2 MR3 MR4 MR5 MR6 MR7 MR8 MR9
angry 0.57 -0.17 -0.04 -0.07 0.05 0.16 0.11 0.06 -0.04
beliefs 0.54 0.23 -0.07 -0.09 0.05 0.24 -0.12 -0.05 -0.01
calm 0.55 -0.07 0.01 0.02 -0.10 -0.09 -0.14 0.05 -0.01
choices 0.43 0.05 0.26 -0.38 -0.07 0.20 -0.05 -0.13 0.11
communicating 0.09 0.30 0.16 0.30 -0.17 0.03 0.19 0.11 0.29
computations -0.01 0.80 -0.04 0.02 0.08 0.10 -0.09 0.03 0.10
conscious 0.36 0.08 0.48 0.21 -0.14 0.01 0.16 -0.12 -0.27
depressed 0.69 -0.11 -0.13 0.05 -0.06 -0.13 0.12 0.06 0.21
depth 0.14 0.22 0.36 0.12 0.34 0.02 -0.14 -0.07 -0.02
desires 0.52 -0.19 -0.03 -0.01 -0.08 0.21 -0.07 -0.04 0.02
disrespected 0.69 0.00 -0.11 -0.07 -0.03 -0.02 0.16 -0.16 -0.09
embarrassed 0.53 0.03 -0.30 0.10 0.22 0.34 0.23 -0.06 -0.01
emo_recog 0.34 0.50 -0.05 0.09 0.11 -0.07 0.22 0.14 -0.09
fear 0.55 -0.37 0.11 0.11 -0.08 0.03 0.01 0.17 0.09
free_will 0.49 0.00 0.32 -0.22 -0.09 0.11 -0.21 -0.18 0.08
goal 0.35 0.31 -0.03 -0.09 -0.06 0.10 -0.11 0.22 0.02
guilt 0.58 0.07 -0.18 0.13 0.25 0.19 -0.07 -0.10 -0.07
happy 0.72 0.09 -0.20 0.18 -0.20 -0.06 -0.01 -0.13 0.09
hungry 0.38 -0.77 0.22 0.02 0.14 -0.04 0.01 -0.08 0.06
intentions 0.35 0.40 0.15 -0.24 0.11 0.03 0.02 -0.12 0.09
joy 0.70 0.00 -0.26 0.10 -0.18 -0.10 -0.03 -0.05 0.06
love 0.60 0.03 -0.20 0.11 0.03 -0.11 0.09 -0.19 -0.05
morality 0.44 0.37 -0.13 -0.08 0.21 -0.26 -0.05 0.26 -0.18
nauseated 0.30 -0.43 0.06 0.11 0.27 0.05 0.00 0.07 0.16
odors 0.15 -0.53 0.38 0.06 0.09 0.12 -0.06 0.16 0.06
pain 0.45 -0.64 0.21 -0.06 0.07 -0.09 -0.05 -0.04 -0.01
personality 0.54 0.29 -0.01 -0.12 0.20 0.12 0.01 0.18 -0.01
pleasure 0.60 0.04 -0.18 0.10 -0.23 -0.17 -0.31 0.04 0.01
pride 0.68 0.14 -0.31 0.06 -0.05 -0.02 0.01 -0.04 -0.01
reasoning 0.23 0.28 0.36 0.05 -0.16 -0.01 -0.05 0.01 0.05
recognizing 0.20 0.32 0.13 0.10 0.11 0.00 -0.11 0.21 0.06
remembering 0.06 0.58 0.17 0.13 -0.05 0.00 0.01 -0.17 0.19
safe 0.58 -0.08 0.23 0.06 -0.06 -0.06 -0.19 0.16 -0.24
seeing -0.07 0.13 0.26 -0.03 -0.19 -0.08 0.19 -0.08 0.02
self_aware 0.27 0.21 0.46 -0.03 -0.10 0.09 0.23 0.00 -0.31
self_restraint 0.34 0.19 0.15 -0.56 0.11 -0.33 0.20 0.05 0.17
sounds -0.07 0.10 0.40 0.10 -0.18 0.23 0.08 0.23 0.14
temperature -0.05 0.34 0.41 0.31 0.34 -0.26 -0.15 -0.23 0.08
thoughts 0.57 0.01 0.18 -0.05 -0.11 -0.16 -0.03 0.03 -0.09
tired 0.39 -0.35 0.07 0.11 0.16 -0.16 0.24 0.06 0.12
MR10 MR11 MR12 MR13 h2 u2 com
angry 0.01 -0.04 0.02 -0.16 0.44 0.56 1.7
beliefs 0.13 0.06 0.08 0.21 0.50 0.50 2.8
calm -0.18 0.10 -0.02 0.10 0.40 0.60 1.8
choices 0.05 -0.15 0.16 -0.09 0.54 0.46 4.6
communicating 0.04 0.04 -0.02 -0.12 0.40 0.60 5.9
computations -0.11 0.08 0.10 0.00 0.71 0.29 1.2
conscious -0.14 -0.04 -0.12 0.01 0.58 0.42 4.2
depressed -0.09 -0.15 -0.01 0.00 0.63 0.37 1.7
depth 0.02 0.17 -0.15 -0.07 0.41 0.59 4.8
desires 0.10 0.03 -0.17 0.15 0.42 0.58 2.3
disrespected 0.03 0.10 0.09 0.08 0.58 0.42 1.4
embarrassed -0.03 0.02 -0.13 -0.05 0.63 0.37 3.7
emo_recog 0.07 -0.02 0.13 0.05 0.49 0.51 3.1
fear 0.03 -0.06 -0.25 -0.09 0.58 0.42 2.9
free_will 0.03 -0.02 -0.03 -0.10 0.51 0.49 3.5
goal -0.18 -0.25 -0.04 0.14 0.42 0.58 5.4
guilt -0.08 0.14 -0.05 -0.03 0.53 0.47 2.4
happy -0.03 0.12 0.05 -0.09 0.69 0.31 1.8
hungry 0.05 0.00 0.03 0.01 0.81 0.19 1.8
intentions -0.14 0.08 0.05 -0.12 0.43 0.57 4.2
joy -0.01 0.05 0.11 0.02 0.62 0.38 1.6
love 0.05 0.07 0.03 0.14 0.50 0.50 1.9
morality 0.33 0.07 0.03 -0.09 0.69 0.31 5.9
nauseated 0.06 -0.10 0.02 0.08 0.42 0.58 3.6
odors 0.03 0.19 0.09 -0.06 0.55 0.45 3.0
pain 0.10 -0.01 0.06 0.08 0.70 0.30 2.3
personality 0.06 -0.09 0.00 0.09 0.50 0.50 2.6
pleasure -0.04 0.08 -0.09 -0.02 0.60 0.40 2.5
pride -0.04 -0.03 0.01 -0.15 0.61 0.39 1.7
reasoning 0.12 -0.06 -0.11 0.22 0.37 0.63 4.8
recognizing 0.00 0.04 -0.14 0.05 0.26 0.74 4.7
remembering 0.28 -0.24 -0.03 -0.08 0.59 0.41 2.8
safe 0.00 -0.10 0.09 -0.22 0.59 0.41 2.8
seeing 0.27 0.20 -0.08 0.02 0.30 0.70 6.0
self_aware -0.10 -0.09 -0.03 0.01 0.52 0.48 4.2
self_restraint -0.16 0.15 -0.19 0.01 0.76 0.24 4.5
sounds -0.08 0.25 0.23 0.06 0.47 0.53 5.6
temperature -0.15 -0.09 0.08 0.04 0.68 0.32 6.4
thoughts 0.03 -0.03 0.15 0.08 0.44 0.56 1.8
tired -0.07 -0.11 0.12 0.05 0.45 0.55 4.7
MR1 MR2 MR3 MR4 MR5 MR6 MR7 MR8 MR9 MR10
SS loadings 8.25 4.13 2.18 1.05 0.96 0.84 0.73 0.66 0.62 0.54
Proportion Var 0.21 0.10 0.05 0.03 0.02 0.02 0.02 0.02 0.02 0.01
Cumulative Var 0.21 0.31 0.36 0.39 0.41 0.44 0.45 0.47 0.49 0.50
Proportion Explained 0.39 0.19 0.10 0.05 0.05 0.04 0.03 0.03 0.03 0.03
Cumulative Proportion 0.39 0.58 0.68 0.73 0.78 0.82 0.85 0.88 0.91 0.94
MR11 MR12 MR13
SS loadings 0.52 0.44 0.40
Proportion Var 0.01 0.01 0.01
Cumulative Var 0.51 0.52 0.53
Proportion Explained 0.02 0.02 0.02
Cumulative Proportion 0.96 0.98 1.00
Mean item complexity = 3.4
Test of the hypothesis that 13 factors are sufficient.
The degrees of freedom for the null model are 780 and the objective function was 17.08 with Chi Square of 3157.08
The degrees of freedom for the model are 338 and the objective function was 1.79
The root mean square of the residuals (RMSR) is 0.02
The df corrected root mean square of the residuals is 0.03
The harmonic number of observations is 198 with the empirical chi square 142.71 with prob < 1
The total number of observations was 200 with Likelihood Chi Square = 314.7 with prob < 0.81
Tucker Lewis Index of factoring reliability = 1.024
RMSEA index = 0.016 and the 90 % confidence intervals are 0 0.018
BIC = -1476.13
Fit based upon off diagonal values = 0.99
Measures of factor score adequacy
MR1 MR2 MR3 MR4 MR5
Correlation of scores with factors 0.98 0.96 0.91 0.86 0.83
Multiple R square of scores with factors 0.95 0.93 0.83 0.74 0.69
Minimum correlation of possible factor scores 0.90 0.86 0.67 0.48 0.38
MR6 MR7 MR8 MR9 MR10
Correlation of scores with factors 0.83 0.79 0.77 0.77 0.75
Multiple R square of scores with factors 0.68 0.62 0.60 0.59 0.56
Minimum correlation of possible factor scores 0.36 0.23 0.19 0.18 0.12
MR11 MR12 MR13
Correlation of scores with factors 0.73 0.71 0.67
Multiple R square of scores with factors 0.53 0.50 0.45
Minimum correlation of possible factor scores 0.06 0.00 -0.10
Factor Analysis using method = minres
Call: fa(r = d2_all, nfactors = 13, rotate = "none", fm = "minres",
cor = chosenCorType)
Standardized loadings (pattern matrix) based upon correlation matrix
MR1 MR2 MR3 MR4 MR5 MR6 MR7 MR8 MR9
angry 0.57 -0.17 -0.04 -0.07 0.05 0.16 0.11 0.06 -0.04
beliefs 0.54 0.23 -0.07 -0.09 0.05 0.24 -0.12 -0.05 -0.01
calm 0.55 -0.07 0.01 0.02 -0.10 -0.09 -0.14 0.05 -0.01
choices 0.43 0.05 0.26 -0.38 -0.07 0.20 -0.05 -0.13 0.11
communicating 0.09 0.30 0.16 0.30 -0.17 0.03 0.19 0.11 0.29
computations -0.01 0.80 -0.04 0.02 0.08 0.10 -0.09 0.03 0.10
conscious 0.36 0.08 0.48 0.21 -0.14 0.01 0.16 -0.12 -0.27
depressed 0.69 -0.11 -0.13 0.05 -0.06 -0.13 0.12 0.06 0.21
depth 0.14 0.22 0.36 0.12 0.34 0.02 -0.14 -0.07 -0.02
desires 0.52 -0.19 -0.03 -0.01 -0.08 0.21 -0.07 -0.04 0.02
disrespected 0.69 0.00 -0.11 -0.07 -0.03 -0.02 0.16 -0.16 -0.09
embarrassed 0.53 0.03 -0.30 0.10 0.22 0.34 0.23 -0.06 -0.01
emo_recog 0.34 0.50 -0.05 0.09 0.11 -0.07 0.22 0.14 -0.09
fear 0.55 -0.37 0.11 0.11 -0.08 0.03 0.01 0.17 0.09
free_will 0.49 0.00 0.32 -0.22 -0.09 0.11 -0.21 -0.18 0.08
goal 0.35 0.31 -0.03 -0.09 -0.06 0.10 -0.11 0.22 0.02
guilt 0.58 0.07 -0.18 0.13 0.25 0.19 -0.07 -0.10 -0.07
happy 0.72 0.09 -0.20 0.18 -0.20 -0.06 -0.01 -0.13 0.09
hungry 0.38 -0.77 0.22 0.02 0.14 -0.04 0.01 -0.08 0.06
intentions 0.35 0.40 0.15 -0.24 0.11 0.03 0.02 -0.12 0.09
joy 0.70 0.00 -0.26 0.10 -0.18 -0.10 -0.03 -0.05 0.06
love 0.60 0.03 -0.20 0.11 0.03 -0.11 0.09 -0.19 -0.05
morality 0.44 0.37 -0.13 -0.08 0.21 -0.26 -0.05 0.26 -0.18
nauseated 0.30 -0.43 0.06 0.11 0.27 0.05 0.00 0.07 0.16
odors 0.15 -0.53 0.38 0.06 0.09 0.12 -0.06 0.16 0.06
pain 0.45 -0.64 0.21 -0.06 0.07 -0.09 -0.05 -0.04 -0.01
personality 0.54 0.29 -0.01 -0.12 0.20 0.12 0.01 0.18 -0.01
pleasure 0.60 0.04 -0.18 0.10 -0.23 -0.17 -0.31 0.04 0.01
pride 0.68 0.14 -0.31 0.06 -0.05 -0.02 0.01 -0.04 -0.01
reasoning 0.23 0.28 0.36 0.05 -0.16 -0.01 -0.05 0.01 0.05
recognizing 0.20 0.32 0.13 0.10 0.11 0.00 -0.11 0.21 0.06
remembering 0.06 0.58 0.17 0.13 -0.05 0.00 0.01 -0.17 0.19
safe 0.58 -0.08 0.23 0.06 -0.06 -0.06 -0.19 0.16 -0.24
seeing -0.07 0.13 0.26 -0.03 -0.19 -0.08 0.19 -0.08 0.02
self_aware 0.27 0.21 0.46 -0.03 -0.10 0.09 0.23 0.00 -0.31
self_restraint 0.34 0.19 0.15 -0.56 0.11 -0.33 0.20 0.05 0.17
sounds -0.07 0.10 0.40 0.10 -0.18 0.23 0.08 0.23 0.14
temperature -0.05 0.34 0.41 0.31 0.34 -0.26 -0.15 -0.23 0.08
thoughts 0.57 0.01 0.18 -0.05 -0.11 -0.16 -0.03 0.03 -0.09
tired 0.39 -0.35 0.07 0.11 0.16 -0.16 0.24 0.06 0.12
MR10 MR11 MR12 MR13 h2 u2 com
angry 0.01 -0.04 0.02 -0.16 0.44 0.56 1.7
beliefs 0.13 0.06 0.08 0.21 0.50 0.50 2.8
calm -0.18 0.10 -0.02 0.10 0.40 0.60 1.8
choices 0.05 -0.15 0.16 -0.09 0.54 0.46 4.6
communicating 0.04 0.04 -0.02 -0.12 0.40 0.60 5.9
computations -0.11 0.08 0.10 0.00 0.71 0.29 1.2
conscious -0.14 -0.04 -0.12 0.01 0.58 0.42 4.2
depressed -0.09 -0.15 -0.01 0.00 0.63 0.37 1.7
depth 0.02 0.17 -0.15 -0.07 0.41 0.59 4.8
desires 0.10 0.03 -0.17 0.15 0.42 0.58 2.3
disrespected 0.03 0.10 0.09 0.08 0.58 0.42 1.4
embarrassed -0.03 0.02 -0.13 -0.05 0.63 0.37 3.7
emo_recog 0.07 -0.02 0.13 0.05 0.49 0.51 3.1
fear 0.03 -0.06 -0.25 -0.09 0.58 0.42 2.9
free_will 0.03 -0.02 -0.03 -0.10 0.51 0.49 3.5
goal -0.18 -0.25 -0.04 0.14 0.42 0.58 5.4
guilt -0.08 0.14 -0.05 -0.03 0.53 0.47 2.4
happy -0.03 0.12 0.05 -0.09 0.69 0.31 1.8
hungry 0.05 0.00 0.03 0.01 0.81 0.19 1.8
intentions -0.14 0.08 0.05 -0.12 0.43 0.57 4.2
joy -0.01 0.05 0.11 0.02 0.62 0.38 1.6
love 0.05 0.07 0.03 0.14 0.50 0.50 1.9
morality 0.33 0.07 0.03 -0.09 0.69 0.31 5.9
nauseated 0.06 -0.10 0.02 0.08 0.42 0.58 3.6
odors 0.03 0.19 0.09 -0.06 0.55 0.45 3.0
pain 0.10 -0.01 0.06 0.08 0.70 0.30 2.3
personality 0.06 -0.09 0.00 0.09 0.50 0.50 2.6
pleasure -0.04 0.08 -0.09 -0.02 0.60 0.40 2.5
pride -0.04 -0.03 0.01 -0.15 0.61 0.39 1.7
reasoning 0.12 -0.06 -0.11 0.22 0.37 0.63 4.8
recognizing 0.00 0.04 -0.14 0.05 0.26 0.74 4.7
remembering 0.28 -0.24 -0.03 -0.08 0.59 0.41 2.8
safe 0.00 -0.10 0.09 -0.22 0.59 0.41 2.8
seeing 0.27 0.20 -0.08 0.02 0.30 0.70 6.0
self_aware -0.10 -0.09 -0.03 0.01 0.52 0.48 4.2
self_restraint -0.16 0.15 -0.19 0.01 0.76 0.24 4.5
sounds -0.08 0.25 0.23 0.06 0.47 0.53 5.6
temperature -0.15 -0.09 0.08 0.04 0.68 0.32 6.4
thoughts 0.03 -0.03 0.15 0.08 0.44 0.56 1.8
tired -0.07 -0.11 0.12 0.05 0.45 0.55 4.7
MR1 MR2 MR3 MR4 MR5 MR6 MR7 MR8 MR9 MR10
SS loadings 8.25 4.13 2.18 1.05 0.96 0.84 0.73 0.66 0.62 0.54
Proportion Var 0.21 0.10 0.05 0.03 0.02 0.02 0.02 0.02 0.02 0.01
Cumulative Var 0.21 0.31 0.36 0.39 0.41 0.44 0.45 0.47 0.49 0.50
Proportion Explained 0.39 0.19 0.10 0.05 0.05 0.04 0.03 0.03 0.03 0.03
Cumulative Proportion 0.39 0.58 0.68 0.73 0.78 0.82 0.85 0.88 0.91 0.94
MR11 MR12 MR13
SS loadings 0.52 0.44 0.40
Proportion Var 0.01 0.01 0.01
Cumulative Var 0.51 0.52 0.53
Proportion Explained 0.02 0.02 0.02
Cumulative Proportion 0.96 0.98 1.00
Mean item complexity = 3.4
Test of the hypothesis that 13 factors are sufficient.
The degrees of freedom for the null model are 780 and the objective function was 17.08 with Chi Square of 3157.08
The degrees of freedom for the model are 338 and the objective function was 1.79
The root mean square of the residuals (RMSR) is 0.02
The df corrected root mean square of the residuals is 0.03
The harmonic number of observations is 198 with the empirical chi square 142.71 with prob < 1
The total number of observations was 200 with Likelihood Chi Square = 314.7 with prob < 0.81
Tucker Lewis Index of factoring reliability = 1.024
RMSEA index = 0.016 and the 90 % confidence intervals are 0 0.018
BIC = -1476.13
Fit based upon off diagonal values = 0.99
Measures of factor score adequacy
MR1 MR2 MR3 MR4 MR5
Correlation of scores with factors 0.98 0.96 0.91 0.86 0.83
Multiple R square of scores with factors 0.95 0.93 0.83 0.74 0.69
Minimum correlation of possible factor scores 0.90 0.86 0.67 0.48 0.38
MR6 MR7 MR8 MR9 MR10
Correlation of scores with factors 0.83 0.79 0.77 0.77 0.75
Multiple R square of scores with factors 0.68 0.62 0.60 0.59 0.56
Minimum correlation of possible factor scores 0.36 0.23 0.19 0.18 0.12
MR11 MR12 MR13
Correlation of scores with factors 0.73 0.71 0.67
Multiple R square of scores with factors 0.53 0.50 0.45
Minimum correlation of possible factor scores 0.06 0.00 -0.10
[1] 3convergence not obtained in GPFoblq. 1000 iterations used.[1] 3Step 2: Run EFA with rotation
Factor Analysis using method = minres
Call: fa(r = d2_all, nfactors = nfactors_d2_all, rotate = chosenRotType,
fm = "minres", cor = chosenCorType)
Standardized loadings (pattern matrix) based upon correlation matrix
MR1 MR2 MR3 h2 u2 com
angry 0.48 0.26 0.05 0.356 0.64 1.6
beliefs 0.51 -0.12 0.16 0.336 0.66 1.3
calm 0.44 0.17 0.12 0.299 0.70 1.5
choices 0.19 0.13 0.36 0.231 0.77 1.8
communicating 0.01 -0.20 0.26 0.111 0.89 1.9
computations 0.12 -0.76 0.25 0.650 0.35 1.3
conscious 0.00 0.16 0.56 0.331 0.67 1.2
depressed 0.65 0.20 0.02 0.504 0.50 1.2
depth -0.09 -0.04 0.45 0.182 0.82 1.1
desires 0.42 0.27 0.04 0.301 0.70 1.7
disrespected 0.65 0.11 0.08 0.488 0.51 1.1
embarrassed 0.62 -0.02 -0.12 0.346 0.65 1.1
emo_recog 0.38 -0.40 0.22 0.360 0.64 2.6
fear 0.33 0.49 0.12 0.441 0.56 1.9
free_will 0.19 0.21 0.43 0.331 0.67 1.9
goal 0.35 -0.21 0.17 0.212 0.79 2.2
guilt 0.60 -0.01 0.01 0.358 0.64 1.0
happy 0.74 0.00 0.02 0.555 0.44 1.0
hungry 0.06 0.87 0.04 0.786 0.21 1.0
intentions 0.24 -0.24 0.37 0.293 0.71 2.5
joy 0.76 0.05 -0.07 0.555 0.44 1.0
love 0.64 0.03 -0.03 0.403 0.60 1.0
morality 0.47 -0.26 0.13 0.306 0.69 1.8
nauseated 0.15 0.47 -0.02 0.270 0.73 1.2
odors -0.20 0.64 0.22 0.425 0.57 1.4
pain 0.15 0.77 0.10 0.662 0.34 1.1
personality 0.49 -0.15 0.24 0.372 0.63 1.7
pleasure 0.61 0.03 0.00 0.375 0.63 1.0
pride 0.81 -0.11 -0.08 0.594 0.41 1.1
reasoning -0.02 -0.08 0.51 0.265 0.73 1.0
recognizing 0.12 -0.20 0.29 0.154 0.85 2.1
remembering 0.02 -0.44 0.36 0.333 0.67 1.9
safe 0.31 0.27 0.34 0.380 0.62 2.9
seeing -0.21 -0.04 0.27 0.083 0.92 1.9
self_aware -0.05 0.02 0.57 0.303 0.70 1.0
self_restraint 0.21 -0.04 0.26 0.144 0.86 2.0
sounds -0.28 0.03 0.37 0.144 0.86 1.9
temperature -0.23 -0.17 0.43 0.212 0.79 1.9
thoughts 0.35 0.17 0.34 0.364 0.64 2.5
tired 0.23 0.42 0.04 0.266 0.73 1.6
MR1 MR2 MR3
SS loadings 6.84 4.15 3.09
Proportion Var 0.17 0.10 0.08
Cumulative Var 0.17 0.27 0.35
Proportion Explained 0.49 0.29 0.22
Cumulative Proportion 0.49 0.78 1.00
With factor correlations of
MR1 MR2 MR3
MR1 1.00 0.16 0.34
MR2 0.16 1.00 -0.02
MR3 0.34 -0.02 1.00
Mean item complexity = 1.6
Test of the hypothesis that 3 factors are sufficient.
The degrees of freedom for the null model are 780 and the objective function was 17.08 with Chi Square of 3157.08
The degrees of freedom for the model are 663 and the objective function was 4.79
The root mean square of the residuals (RMSR) is 0.05
The df corrected root mean square of the residuals is 0.05
The harmonic number of observations is 198 with the empirical chi square 788.89 with prob < 0.00052
The total number of observations was 200 with Likelihood Chi Square = 874.92 with prob < 5.4e-08
Tucker Lewis Index of factoring reliability = 0.894
RMSEA index = 0.047 and the 90 % confidence intervals are 0.032 0.047
BIC = -2637.86
Fit based upon off diagonal values = 0.95
Measures of factor score adequacy
MR1 MR2 MR3
Correlation of scores with factors 0.96 0.96 0.90
Multiple R square of scores with factors 0.92 0.92 0.81
Minimum correlation of possible factor scores 0.85 0.84 0.62Factor loadings table
Study 3 (CogSci 2018)
Study information:
- Design: 7 conditions (beetle, robot, …) + 2 follow-up conditions, 3-point response scale, “decide what to do” and “have intentions”
- Population: 7-9yo children
- Date conducted: January - June 2017
Demographics
Joining, by = c("character", "min_age", "max_age", "median_age", "mean_age", "sd_age")
Column `character` joining factor and character vector, coercing into character vectorExploratory factor analysis
Step 1: Run maximal EFA (without and with rotation)
Factor Analysis using method = minres
Call: fa(r = d3_all, nfactors = 6, rotate = "none", fm = "minres",
cor = chosenCorType)
Standardized loadings (pattern matrix) based upon correlation matrix
MR1 MR2 MR3 MR4 MR5 MR6 h2 u2 com
angry 0.80 -0.08 0.00 -0.04 -0.36 -0.05 0.78 0.22 1.4
choices 0.56 0.52 0.15 -0.03 -0.03 -0.06 0.61 0.39 2.2
conscious 0.48 0.35 0.09 0.02 0.04 0.04 0.37 0.63 2.0
depressed 0.71 -0.23 0.21 -0.20 -0.15 0.16 0.69 0.31 1.8
depth 0.33 0.45 0.06 -0.17 0.24 0.16 0.43 0.57 3.2
disrespected 0.62 -0.23 0.27 -0.03 -0.01 0.27 0.59 0.41 2.1
embarrassed 0.53 -0.17 0.43 0.06 0.10 0.09 0.52 0.48 2.3
fear 0.83 -0.15 -0.27 0.11 0.10 -0.05 0.80 0.20 1.4
guilt 0.54 -0.33 0.46 0.38 -0.03 -0.07 0.76 0.24 3.6
happy 0.69 -0.18 -0.02 -0.27 -0.05 -0.15 0.60 0.40 1.6
hungry 0.75 -0.01 -0.54 0.10 -0.02 -0.03 0.86 0.14 1.9
love 0.65 -0.25 0.06 0.14 0.27 0.02 0.58 0.42 1.8
nauseated 0.52 0.10 -0.25 0.09 -0.21 0.34 0.50 0.50 2.8
odors 0.64 0.02 -0.42 0.14 -0.04 -0.10 0.61 0.39 1.9
pain 0.75 -0.15 -0.28 -0.08 0.10 -0.04 0.67 0.33 1.4
pride 0.72 -0.26 0.27 -0.23 0.02 -0.28 0.79 0.21 2.2
reasoning 0.39 0.61 0.07 0.06 0.06 -0.09 0.54 0.46 1.9
remembering 0.33 0.58 0.27 0.22 -0.15 -0.11 0.60 0.40 2.7
temperature 0.39 0.52 0.07 -0.14 -0.02 0.02 0.45 0.55 2.1
tired 0.77 0.07 -0.10 0.01 0.18 0.06 0.65 0.35 1.2
MR1 MR2 MR3 MR4 MR5 MR6
SS loadings 7.66 2.04 1.39 0.49 0.43 0.40
Proportion Var 0.38 0.10 0.07 0.02 0.02 0.02
Cumulative Var 0.38 0.49 0.55 0.58 0.60 0.62
Proportion Explained 0.62 0.16 0.11 0.04 0.03 0.03
Cumulative Proportion 0.62 0.78 0.89 0.93 0.97 1.00
Mean item complexity = 2.1
Test of the hypothesis that 6 factors are sufficient.
The degrees of freedom for the null model are 190 and the objective function was 12.17 with Chi Square of 1393.17
The degrees of freedom for the model are 85 and the objective function was 0.93
The root mean square of the residuals (RMSR) is 0.03
The df corrected root mean square of the residuals is 0.04
The harmonic number of observations is 123 with the empirical chi square 32.36 with prob < 1
The total number of observations was 123 with Likelihood Chi Square = 103.04 with prob < 0.089
Tucker Lewis Index of factoring reliability = 0.965
RMSEA index = 0.053 and the 90 % confidence intervals are 0 0.068
BIC = -306
Fit based upon off diagonal values = 1
Measures of factor score adequacy
MR1 MR2 MR3 MR4 MR5
Correlation of scores with factors 0.98 0.91 0.92 0.79 0.76
Multiple R square of scores with factors 0.96 0.83 0.85 0.62 0.57
Minimum correlation of possible factor scores 0.92 0.66 0.69 0.25 0.14
MR6
Correlation of scores with factors 0.72
Multiple R square of scores with factors 0.52
Minimum correlation of possible factor scores 0.05
Factor Analysis using method = minres
Call: fa(r = d3_all, nfactors = 6, rotate = "none", fm = "minres",
cor = chosenCorType)
Standardized loadings (pattern matrix) based upon correlation matrix
MR1 MR2 MR3 MR4 MR5 MR6 h2 u2 com
angry 0.80 -0.08 0.00 -0.04 -0.36 -0.05 0.78 0.22 1.4
choices 0.56 0.52 0.15 -0.03 -0.03 -0.06 0.61 0.39 2.2
conscious 0.48 0.35 0.09 0.02 0.04 0.04 0.37 0.63 2.0
depressed 0.71 -0.23 0.21 -0.20 -0.15 0.16 0.69 0.31 1.8
depth 0.33 0.45 0.06 -0.17 0.24 0.16 0.43 0.57 3.2
disrespected 0.62 -0.23 0.27 -0.03 -0.01 0.27 0.59 0.41 2.1
embarrassed 0.53 -0.17 0.43 0.06 0.10 0.09 0.52 0.48 2.3
fear 0.83 -0.15 -0.27 0.11 0.10 -0.05 0.80 0.20 1.4
guilt 0.54 -0.33 0.46 0.38 -0.03 -0.07 0.76 0.24 3.6
happy 0.69 -0.18 -0.02 -0.27 -0.05 -0.15 0.60 0.40 1.6
hungry 0.75 -0.01 -0.54 0.10 -0.02 -0.03 0.86 0.14 1.9
love 0.65 -0.25 0.06 0.14 0.27 0.02 0.58 0.42 1.8
nauseated 0.52 0.10 -0.25 0.09 -0.21 0.34 0.50 0.50 2.8
odors 0.64 0.02 -0.42 0.14 -0.04 -0.10 0.61 0.39 1.9
pain 0.75 -0.15 -0.28 -0.08 0.10 -0.04 0.67 0.33 1.4
pride 0.72 -0.26 0.27 -0.23 0.02 -0.28 0.79 0.21 2.2
reasoning 0.39 0.61 0.07 0.06 0.06 -0.09 0.54 0.46 1.9
remembering 0.33 0.58 0.27 0.22 -0.15 -0.11 0.60 0.40 2.7
temperature 0.39 0.52 0.07 -0.14 -0.02 0.02 0.45 0.55 2.1
tired 0.77 0.07 -0.10 0.01 0.18 0.06 0.65 0.35 1.2
MR1 MR2 MR3 MR4 MR5 MR6
SS loadings 7.66 2.04 1.39 0.49 0.43 0.40
Proportion Var 0.38 0.10 0.07 0.02 0.02 0.02
Cumulative Var 0.38 0.49 0.55 0.58 0.60 0.62
Proportion Explained 0.62 0.16 0.11 0.04 0.03 0.03
Cumulative Proportion 0.62 0.78 0.89 0.93 0.97 1.00
Mean item complexity = 2.1
Test of the hypothesis that 6 factors are sufficient.
The degrees of freedom for the null model are 190 and the objective function was 12.17 with Chi Square of 1393.17
The degrees of freedom for the model are 85 and the objective function was 0.93
The root mean square of the residuals (RMSR) is 0.03
The df corrected root mean square of the residuals is 0.04
The harmonic number of observations is 123 with the empirical chi square 32.36 with prob < 1
The total number of observations was 123 with Likelihood Chi Square = 103.04 with prob < 0.089
Tucker Lewis Index of factoring reliability = 0.965
RMSEA index = 0.053 and the 90 % confidence intervals are 0 0.068
BIC = -306
Fit based upon off diagonal values = 1
Measures of factor score adequacy
MR1 MR2 MR3 MR4 MR5
Correlation of scores with factors 0.98 0.91 0.92 0.79 0.76
Multiple R square of scores with factors 0.96 0.83 0.85 0.62 0.57
Minimum correlation of possible factor scores 0.92 0.66 0.69 0.25 0.14
MR6
Correlation of scores with factors 0.72
Multiple R square of scores with factors 0.52
Minimum correlation of possible factor scores 0.05
[1] 3[1] 3Step 2: Run EFA with rotation
A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully.The estimated weights for the factor scores are probably incorrect. Try a different factor extraction method.
A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully.The estimated weights for the factor scores are probably incorrect. Try a different factor extraction method.
A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully.The estimated weights for the factor scores are probably incorrect. Try a different factor extraction method.
A loading greater than abs(1) was detected. Examine the loadings carefully.The estimated weights for the factor scores are probably incorrect. Try a different factor extraction method.
A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully.The estimated weights for the factor scores are probably incorrect. Try a different factor extraction method.
A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully.The estimated weights for the factor scores are probably incorrect. Try a different factor extraction method.
A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully.The estimated weights for the factor scores are probably incorrect. Try a different factor extraction method.
A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully.The estimated weights for the factor scores are probably incorrect. Try a different factor extraction method.
A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully.The estimated weights for the factor scores are probably incorrect. Try a different factor extraction method.
A loading greater than abs(1) was detected. Examine the loadings carefully.The estimated weights for the factor scores are probably incorrect. Try a different factor extraction method.
A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully.The estimated weights for the factor scores are probably incorrect. Try a different factor extraction method.
A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully.The estimated weights for the factor scores are probably incorrect. Try a different factor extraction method.
A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully.The estimated weights for the factor scores are probably incorrect. Try a different factor extraction method.
A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully.The estimated weights for the factor scores are probably incorrect. Try a different factor extraction method.
A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully.The estimated weights for the factor scores are probably incorrect. Try a different factor extraction method.
A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully.The estimated weights for the factor scores are probably incorrect. Try a different factor extraction method.
A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully.The estimated weights for the factor scores are probably incorrect. Try a different factor extraction method.
A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully.The estimated weights for the factor scores are probably incorrect. Try a different factor extraction method.
A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully.The estimated weights for the factor scores are probably incorrect. Try a different factor extraction method.
A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully.The estimated weights for the factor scores are probably incorrect. Try a different factor extraction method.
A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully.The estimated weights for the factor scores are probably incorrect. Try a different factor extraction method.
A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully.The estimated weights for the factor scores are probably incorrect. Try a different factor extraction method.
A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully.The estimated weights for the factor scores are probably incorrect. Try a different factor extraction method.
A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully.The estimated weights for the factor scores are probably incorrect. Try a different factor extraction method.
A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully.The estimated weights for the factor scores are probably incorrect. Try a different factor extraction method.
A loading greater than abs(1) was detected. Examine the loadings carefully.The estimated weights for the factor scores are probably incorrect. Try a different factor extraction method.
A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully.The estimated weights for the factor scores are probably incorrect. Try a different factor extraction method.
The estimated weights for the factor scores are probably incorrect. Try a different factor extraction method.
A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully.The estimated weights for the factor scores are probably incorrect. Try a different factor extraction method.
A loading greater than abs(1) was detected. Examine the loadings carefully.The estimated weights for the factor scores are probably incorrect. Try a different factor extraction method.
A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully.The estimated weights for the factor scores are probably incorrect. Try a different factor extraction method.
A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully.convergence not obtained in GPFoblq. 1000 iterations used. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully.The estimated weights for the factor scores are probably incorrect. Try a different factor extraction method.
A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully.The estimated weights for the factor scores are probably incorrect. Try a different factor extraction method.
A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully.The estimated weights for the factor scores are probably incorrect. Try a different factor extraction method.
A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully.The estimated weights for the factor scores are probably incorrect. Try a different factor extraction method.
An ultra-Heywood case was detected. Examine the results carefully A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully.The estimated weights for the factor scores are probably incorrect. Try a different factor extraction method.
A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully.The estimated weights for the factor scores are probably incorrect. Try a different factor extraction method.
A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully.The estimated weights for the factor scores are probably incorrect. Try a different factor extraction method.
A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully.The estimated weights for the factor scores are probably incorrect. Try a different factor extraction method.
An ultra-Heywood case was detected. Examine the results carefully A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully.The estimated weights for the factor scores are probably incorrect. Try a different factor extraction method.
A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully.The estimated weights for the factor scores are probably incorrect. Try a different factor extraction method.
A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully.The estimated weights for the factor scores are probably incorrect. Try a different factor extraction method.
A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully.The estimated weights for the factor scores are probably incorrect. Try a different factor extraction method.
A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully.The estimated weights for the factor scores are probably incorrect. Try a different factor extraction method.
A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully.The estimated weights for the factor scores are probably incorrect. Try a different factor extraction method.
A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully.The estimated weights for the factor scores are probably incorrect. Try a different factor extraction method.
A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully.The estimated weights for the factor scores are probably incorrect. Try a different factor extraction method.
A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully.The estimated weights for the factor scores are probably incorrect. Try a different factor extraction method.
A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully.The estimated weights for the factor scores are probably incorrect. Try a different factor extraction method.
A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully.The estimated weights for the factor scores are probably incorrect. Try a different factor extraction method.
A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully.The estimated weights for the factor scores are probably incorrect. Try a different factor extraction method.
A loading greater than abs(1) was detected. Examine the loadings carefully.The estimated weights for the factor scores are probably incorrect. Try a different factor extraction method.
A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully.The estimated weights for the factor scores are probably incorrect. Try a different factor extraction method.
A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully.Factor Analysis with confidence intervals using method = fa(r = d3_all, nfactors = nfactors_d3_all, n.iter = 5000, rotate = chosenRotType,
fm = "minres", cor = chosenCorType)
Factor Analysis using method = minres
Call: fa(r = d3_all, nfactors = nfactors_d3_all, n.iter = 5000, rotate = chosenRotType,
fm = "minres", cor = chosenCorType)
Standardized loadings (pattern matrix) based upon correlation matrix
MR1 MR3 MR2 h2 u2 com
angry 0.41 0.43 0.14 0.62 0.38 2.2
choices 0.05 0.09 0.73 0.62 0.38 1.0
conscious 0.09 0.10 0.52 0.37 0.63 1.1
depressed 0.17 0.66 0.03 0.60 0.40 1.1
depth 0.04 -0.04 0.54 0.29 0.71 1.0
disrespected 0.06 0.67 0.02 0.51 0.49 1.0
embarrassed -0.17 0.76 0.10 0.52 0.48 1.1
fear 0.73 0.26 -0.01 0.78 0.22 1.3
guilt -0.11 0.78 -0.04 0.52 0.48 1.0
happy 0.38 0.42 0.01 0.49 0.51 2.0
hungry 0.98 -0.11 0.02 0.87 0.13 1.0
love 0.30 0.51 -0.05 0.48 0.52 1.6
nauseated 0.48 0.00 0.16 0.31 0.69 1.2
odors 0.78 -0.08 0.06 0.58 0.42 1.0
pain 0.69 0.21 -0.03 0.66 0.34 1.2
pride 0.13 0.71 0.02 0.63 0.37 1.1
reasoning 0.04 -0.11 0.74 0.54 0.46 1.0
remembering -0.17 0.05 0.71 0.46 0.54 1.1
temperature 0.05 -0.05 0.65 0.43 0.57 1.0
tired 0.49 0.25 0.25 0.61 0.39 2.1
MR1 MR3 MR2
SS loadings 4.10 3.90 2.88
Proportion Var 0.21 0.19 0.14
Cumulative Var 0.21 0.40 0.54
Proportion Explained 0.38 0.36 0.26
Cumulative Proportion 0.38 0.74 1.00
With factor correlations of
MR1 MR3 MR2
MR1 1.00 0.5 0.36
MR3 0.50 1.0 0.30
MR2 0.36 0.3 1.00
Mean item complexity = 1.3
Test of the hypothesis that 3 factors are sufficient.
The degrees of freedom for the null model are 190 and the objective function was 12.17 with Chi Square of 1393.17
The degrees of freedom for the model are 133 and the objective function was 1.7
The root mean square of the residuals (RMSR) is 0.04
The df corrected root mean square of the residuals is 0.05
The harmonic number of observations is 123 with the empirical chi square 82.37 with prob < 1
The total number of observations was 123 with Likelihood Chi Square = 191.76 with prob < 0.00065
Tucker Lewis Index of factoring reliability = 0.929
RMSEA index = 0.068 and the 90 % confidence intervals are 0.04 0.078
BIC = -448.26
Fit based upon off diagonal values = 0.99
Measures of factor score adequacy
MR1 MR3 MR2
Correlation of scores with factors 0.97 0.94 0.92
Multiple R square of scores with factors 0.94 0.89 0.85
Minimum correlation of possible factor scores 0.89 0.78 0.69
Coefficients and bootstrapped confidence intervals
low MR1 upper low MR3 upper low MR2 upper
angry 0.24 0.41 0.63 0.25 0.43 0.61 0.01 0.14 0.29
choices -0.06 0.05 0.22 -0.06 0.09 0.25 0.58 0.73 0.86
conscious -0.09 0.09 0.30 -0.06 0.10 0.28 0.36 0.52 0.67
depressed 0.02 0.17 0.39 0.46 0.66 0.84 -0.10 0.03 0.17
depth -0.17 0.04 0.27 -0.23 -0.04 0.17 0.34 0.54 0.71
disrespected -0.11 0.06 0.28 0.45 0.67 0.87 -0.10 0.02 0.15
embarrassed -0.30 -0.17 0.02 0.58 0.76 0.88 -0.03 0.10 0.22
fear 0.60 0.73 0.89 0.14 0.26 0.41 -0.10 -0.01 0.10
guilt -0.27 -0.11 0.11 0.61 0.78 0.90 -0.17 -0.04 0.09
happy 0.19 0.38 0.61 0.24 0.42 0.62 -0.14 0.01 0.18
hungry 0.88 0.98 1.03 -0.20 -0.11 0.07 -0.06 0.02 0.14
love 0.12 0.30 0.53 0.32 0.51 0.69 -0.20 -0.05 0.12
nauseated 0.29 0.48 0.68 -0.18 0.00 0.21 -0.01 0.16 0.34
odors 0.63 0.78 0.92 -0.21 -0.08 0.11 -0.06 0.06 0.20
pain 0.53 0.69 0.88 0.08 0.21 0.38 -0.16 -0.03 0.12
pride -0.01 0.13 0.33 0.53 0.71 0.87 -0.10 0.02 0.14
reasoning -0.10 0.04 0.22 -0.25 -0.11 0.04 0.58 0.74 0.87
remembering -0.35 -0.17 0.03 -0.13 0.05 0.22 0.57 0.71 0.84
temperature -0.13 0.05 0.25 -0.20 -0.05 0.11 0.49 0.65 0.80
tired 0.31 0.49 0.71 0.12 0.25 0.41 0.09 0.25 0.41
Interfactor correlations and bootstrapped confidence intervals
lower estimate upper
MR1-MR3 0.282 0.50 0.60
MR1-MR2 0.133 0.36 0.53
MR3-MR2 0.089 0.30 0.45Factor loadings table
Subscales
Reliability (and intercorrelations - see alpha)
Call: scoreItems(keys = keys.list, items = d3_all, min = 0, max = 1)
(Unstandardized) Alpha:
HEART BODY MIND
alpha 0.88 0.91 0.82
Standard errors of unstandardized Alpha:
HEART BODY MIND
ASE 0.036 0.032 0.048
Average item correlation:
HEART BODY MIND
average.r 0.5 0.58 0.43
Guttman 6* reliability:
HEART BODY MIND
Lambda.6 0.89 0.93 0.83
Signal/Noise based upon av.r :
HEART BODY MIND
Signal/Noise 7 9.6 4.5
Scale intercorrelations corrected for attenuation
raw correlations below the diagonal, alpha on the diagonal
corrected correlations above the diagonal:
HEART BODY MIND
HEART 0.88 0.74 0.37
BODY 0.66 0.91 0.50
MIND 0.31 0.43 0.82
In order to see the item by scale loadings and frequency counts of the data
print with the short option = FALSEOmega
Call: omega(m = d3_all, plot = F)
Alpha: 0.92
G.6: 0.94
Omega Hierarchical: 0.64
Omega H asymptotic: 0.68
Omega Total 0.94
Schmid Leiman Factor loadings greater than 0.2
g F1* F2* F3* h2 u2 p2
angry 0.66 0.26 0.33 0.62 0.38 0.70
choices 0.44 0.64 0.62 0.38 0.32
conscious 0.38 0.46 0.37 0.63 0.40
depressed 0.57 0.51 0.60 0.40 0.55
depth 0.26 0.47 0.29 0.71 0.23
disrespected 0.49 0.52 0.51 0.49 0.47
embarrassed 0.40 0.58 0.52 0.48 0.31
fear 0.73 0.46 0.20 0.78 0.22 0.68
guilt 0.40 0.60 0.52 0.48 0.30
happy 0.57 0.24 0.32 0.49 0.51 0.67
hungry 0.70 0.61 0.87 0.13 0.56
love 0.54 0.39 0.48 0.52 0.60
nauseated 0.45 0.30 0.31 0.69 0.65
odors 0.58 0.49 0.58 0.42 0.58
pain 0.66 0.44 0.66 0.34 0.67
pride 0.57 0.55 0.63 0.37 0.51
reasoning 0.31 0.66 0.54 0.46 0.18
remembering 0.23 0.63 0.46 0.54 0.11
temperature 0.31 0.58 0.43 0.57 0.22
tired 0.66 0.30 0.22 0.61 0.39 0.71
With eigenvalues of:
g F1* F2* F3*
5.3 1.4 2.0 2.1
general/max 2.53 max/min = 1.5
mean percent general = 0.47 with sd = 0.2 and cv of 0.42
Explained Common Variance of the general factor = 0.49
The degrees of freedom are 133 and the fit is 1.7
The number of observations was 123 with Chi Square = 191.76 with prob < 0.00065
The root mean square of the residuals is 0.04
The df corrected root mean square of the residuals is 0.05
RMSEA index = 0.068 and the 10 % confidence intervals are 0.04 0.078
BIC = -448.26
Compare this with the adequacy of just a general factor and no group factors
The degrees of freedom for just the general factor are 170 and the fit is 5.12
The number of observations was 123 with Chi Square = 582.57 with prob < 1.3e-46
The root mean square of the residuals is 0.16
The df corrected root mean square of the residuals is 0.17
RMSEA index = 0.148 and the 10 % confidence intervals are 0.129 0.154
BIC = -235.5
Measures of factor score adequacy
g F1* F2* F3*
Correlation of scores with factors 0.83 0.70 0.81 0.86
Multiple R square of scores with factors 0.69 0.49 0.66 0.74
Minimum correlation of factor score estimates 0.38 -0.02 0.32 0.49
Total, General and Subset omega for each subset
g F1* F2* F3*
Omega total for total scores and subscales 0.94 0.89 0.88 0.82
Omega general for total scores and subscales 0.64 0.61 0.49 0.20
Omega group for total scores and subscales 0.22 0.29 0.40 0.63Study 4 (CogSci 2018)
Study information:
- Design: 7 conditions (beetle, robot, …) + 2 follow-up conditions (doll, teddy bear), 3-point response scale, “decide what to do” and “have intentions”
- Population: 4-6yo children
- Date conducted: March 2017 - present
Demographics
Joining, by = c("character", "min_age", "max_age", "median_age", "mean_age", "sd_age")
Column `character` joining factor and character vector, coercing into character vectorExploratory factor analysis
Step 1: Run maximal EFA (without and with rotation)
Factor Analysis using method = minres
Call: fa(r = d4_all, nfactors = 6, rotate = "none", fm = "minres",
cor = chosenCorType)
Standardized loadings (pattern matrix) based upon correlation matrix
MR1 MR2 MR3 MR4 MR5 MR6 h2 u2 com
angry 0.67 -0.02 -0.20 -0.41 0.13 0.02 0.68 0.3238 2.0
choices 0.53 0.08 0.10 0.14 0.21 0.18 0.40 0.6042 1.9
conscious 0.57 0.56 -0.53 0.05 -0.26 0.04 1.00 0.0015 3.4
depressed 0.61 -0.18 -0.20 -0.06 0.29 0.22 0.58 0.4184 2.2
depth 0.43 0.31 0.07 -0.01 0.09 0.08 0.30 0.6981 2.1
disrespected 0.66 -0.06 -0.24 -0.20 0.15 -0.16 0.58 0.4173 1.8
embarrassed 0.55 -0.04 0.07 0.13 -0.05 -0.36 0.46 0.5369 1.9
fear 0.60 -0.13 0.16 0.10 -0.21 0.09 0.47 0.5347 1.6
guilt 0.50 0.17 0.22 0.02 0.15 -0.03 0.35 0.6478 1.9
happy 0.68 -0.19 -0.13 0.40 0.06 -0.07 0.69 0.3080 1.9
hungry 0.74 -0.20 0.22 -0.19 -0.10 -0.04 0.69 0.3127 1.5
love 0.59 -0.27 -0.12 0.36 -0.05 0.15 0.59 0.4133 2.4
nauseated 0.65 -0.19 0.04 -0.03 0.11 -0.24 0.53 0.4679 1.5
odors 0.62 -0.18 0.17 -0.20 -0.31 0.16 0.61 0.3906 2.3
pain 0.53 0.01 -0.11 -0.10 -0.13 -0.21 0.37 0.6327 1.6
pride 0.66 -0.14 -0.12 0.15 -0.09 0.06 0.50 0.4993 1.3
reasoning 0.51 0.23 0.15 -0.15 -0.03 0.03 0.36 0.6428 1.8
remembering 0.41 0.24 0.38 0.06 -0.04 0.02 0.37 0.6262 2.7
temperature 0.50 0.43 0.19 0.20 0.13 -0.09 0.53 0.4659 2.8
tired 0.72 0.03 0.10 -0.11 0.00 0.12 0.56 0.4423 1.2
MR1 MR2 MR3 MR4 MR5 MR6
SS loadings 7.04 1.04 0.86 0.74 0.49 0.44
Proportion Var 0.35 0.05 0.04 0.04 0.02 0.02
Cumulative Var 0.35 0.40 0.45 0.48 0.51 0.53
Proportion Explained 0.66 0.10 0.08 0.07 0.05 0.04
Cumulative Proportion 0.66 0.76 0.84 0.91 0.96 1.00
Mean item complexity = 2
Test of the hypothesis that 6 factors are sufficient.
The degrees of freedom for the null model are 190 and the objective function was 8.76 with Chi Square of 994.76
The degrees of freedom for the model are 85 and the objective function was 0.89
The root mean square of the residuals (RMSR) is 0.03
The df corrected root mean square of the residuals is 0.05
The harmonic number of observations is 120 with the empirical chi square 50.27 with prob < 1
The total number of observations was 122 with Likelihood Chi Square = 97.45 with prob < 0.17
Tucker Lewis Index of factoring reliability = 0.964
RMSEA index = 0.047 and the 90 % confidence intervals are 0 0.063
BIC = -310.9
Fit based upon off diagonal values = 0.99
Measures of factor score adequacy
MR1 MR2 MR3 MR4 MR5
Correlation of scores with factors 0.97 0.90 0.88 0.82 0.77
Multiple R square of scores with factors 0.95 0.81 0.78 0.67 0.59
Minimum correlation of possible factor scores 0.89 0.63 0.56 0.34 0.19
MR6
Correlation of scores with factors 0.69
Multiple R square of scores with factors 0.48
Minimum correlation of possible factor scores -0.05
Factor Analysis using method = minres
Call: fa(r = d4_all, nfactors = 6, rotate = "none", fm = "minres",
cor = chosenCorType)
Standardized loadings (pattern matrix) based upon correlation matrix
MR1 MR2 MR3 MR4 MR5 MR6 h2 u2 com
angry 0.67 -0.02 -0.20 -0.41 0.13 0.02 0.68 0.3238 2.0
choices 0.53 0.08 0.10 0.14 0.21 0.18 0.40 0.6042 1.9
conscious 0.57 0.56 -0.53 0.05 -0.26 0.04 1.00 0.0015 3.4
depressed 0.61 -0.18 -0.20 -0.06 0.29 0.22 0.58 0.4184 2.2
depth 0.43 0.31 0.07 -0.01 0.09 0.08 0.30 0.6981 2.1
disrespected 0.66 -0.06 -0.24 -0.20 0.15 -0.16 0.58 0.4173 1.8
embarrassed 0.55 -0.04 0.07 0.13 -0.05 -0.36 0.46 0.5369 1.9
fear 0.60 -0.13 0.16 0.10 -0.21 0.09 0.47 0.5347 1.6
guilt 0.50 0.17 0.22 0.02 0.15 -0.03 0.35 0.6478 1.9
happy 0.68 -0.19 -0.13 0.40 0.06 -0.07 0.69 0.3080 1.9
hungry 0.74 -0.20 0.22 -0.19 -0.10 -0.04 0.69 0.3127 1.5
love 0.59 -0.27 -0.12 0.36 -0.05 0.15 0.59 0.4133 2.4
nauseated 0.65 -0.19 0.04 -0.03 0.11 -0.24 0.53 0.4679 1.5
odors 0.62 -0.18 0.17 -0.20 -0.31 0.16 0.61 0.3906 2.3
pain 0.53 0.01 -0.11 -0.10 -0.13 -0.21 0.37 0.6327 1.6
pride 0.66 -0.14 -0.12 0.15 -0.09 0.06 0.50 0.4993 1.3
reasoning 0.51 0.23 0.15 -0.15 -0.03 0.03 0.36 0.6428 1.8
remembering 0.41 0.24 0.38 0.06 -0.04 0.02 0.37 0.6262 2.7
temperature 0.50 0.43 0.19 0.20 0.13 -0.09 0.53 0.4659 2.8
tired 0.72 0.03 0.10 -0.11 0.00 0.12 0.56 0.4423 1.2
MR1 MR2 MR3 MR4 MR5 MR6
SS loadings 7.04 1.04 0.86 0.74 0.49 0.44
Proportion Var 0.35 0.05 0.04 0.04 0.02 0.02
Cumulative Var 0.35 0.40 0.45 0.48 0.51 0.53
Proportion Explained 0.66 0.10 0.08 0.07 0.05 0.04
Cumulative Proportion 0.66 0.76 0.84 0.91 0.96 1.00
Mean item complexity = 2
Test of the hypothesis that 6 factors are sufficient.
The degrees of freedom for the null model are 190 and the objective function was 8.76 with Chi Square of 994.76
The degrees of freedom for the model are 85 and the objective function was 0.89
The root mean square of the residuals (RMSR) is 0.03
The df corrected root mean square of the residuals is 0.05
The harmonic number of observations is 120 with the empirical chi square 50.27 with prob < 1
The total number of observations was 122 with Likelihood Chi Square = 97.45 with prob < 0.17
Tucker Lewis Index of factoring reliability = 0.964
RMSEA index = 0.047 and the 90 % confidence intervals are 0 0.063
BIC = -310.9
Fit based upon off diagonal values = 0.99
Measures of factor score adequacy
MR1 MR2 MR3 MR4 MR5
Correlation of scores with factors 0.97 0.90 0.88 0.82 0.77
Multiple R square of scores with factors 0.95 0.81 0.78 0.67 0.59
Minimum correlation of possible factor scores 0.89 0.63 0.56 0.34 0.19
MR6
Correlation of scores with factors 0.69
Multiple R square of scores with factors 0.48
Minimum correlation of possible factor scores -0.05
[1] 2The estimated weights for the factor scores are probably incorrect. Try a different factor extraction method.[1] 2Step 2: Run EFA with rotation
The estimated weights for the factor scores are probably incorrect. Try a different factor extraction method.
The estimated weights for the factor scores are probably incorrect. Try a different factor extraction method.
An ultra-Heywood case was detected. Examine the results carefully A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully.The estimated weights for the factor scores are probably incorrect. Try a different factor extraction method.
An ultra-Heywood case was detected. Examine the results carefully A loading greater than abs(1) was detected. Examine the loadings carefully.The estimated weights for the factor scores are probably incorrect. Try a different factor extraction method.
A loading greater than abs(1) was detected. Examine the loadings carefully. A loading greater than abs(1) was detected. Examine the loadings carefully.The estimated weights for the factor scores are probably incorrect. Try a different factor extraction method.
An ultra-Heywood case was detected. Examine the results carefullyFactor Analysis with confidence intervals using method = fa(r = d4_all, nfactors = nfactors_d4_all, n.iter = 5000, rotate = chosenRotType,
fm = "minres", cor = chosenCorType)
Factor Analysis using method = minres
Call: fa(r = d4_all, nfactors = nfactors_d4_all, n.iter = 5000, rotate = chosenRotType,
fm = "minres", cor = chosenCorType)
Standardized loadings (pattern matrix) based upon correlation matrix
MR1 MR2 h2 u2 com
angry 0.61 0.08 0.44 0.56 1.0
choices 0.31 0.29 0.29 0.71 2.0
conscious 0.28 0.32 0.29 0.71 2.0
depressed 0.67 -0.07 0.40 0.60 1.0
depth 0.03 0.52 0.30 0.70 1.0
disrespected 0.66 0.01 0.45 0.55 1.0
embarrassed 0.45 0.14 0.30 0.70 1.2
fear 0.54 0.09 0.36 0.64 1.1
guilt 0.16 0.46 0.32 0.68 1.2
happy 0.73 -0.05 0.49 0.51 1.0
hungry 0.68 0.10 0.55 0.45 1.0
love 0.71 -0.15 0.40 0.60 1.1
nauseated 0.66 0.01 0.45 0.55 1.0
odors 0.57 0.06 0.37 0.63 1.0
pain 0.47 0.09 0.28 0.72 1.1
pride 0.72 -0.05 0.47 0.53 1.0
reasoning 0.16 0.47 0.34 0.66 1.2
remembering -0.03 0.57 0.31 0.69 1.0
temperature -0.03 0.70 0.46 0.54 1.0
tired 0.51 0.30 0.53 0.47 1.6
MR1 MR2
SS loadings 5.58 2.22
Proportion Var 0.28 0.11
Cumulative Var 0.28 0.39
Proportion Explained 0.72 0.28
Cumulative Proportion 0.72 1.00
With factor correlations of
MR1 MR2
MR1 1.0 0.6
MR2 0.6 1.0
Mean item complexity = 1.2
Test of the hypothesis that 2 factors are sufficient.
The degrees of freedom for the null model are 190 and the objective function was 8.76 with Chi Square of 994.76
The degrees of freedom for the model are 151 and the objective function was 2.16
The root mean square of the residuals (RMSR) is 0.06
The df corrected root mean square of the residuals is 0.07
The harmonic number of observations is 120 with the empirical chi square 177.45 with prob < 0.07
The total number of observations was 122 with Likelihood Chi Square = 242.36 with prob < 3.4e-06
Tucker Lewis Index of factoring reliability = 0.855
RMSEA index = 0.078 and the 90 % confidence intervals are 0.054 0.087
BIC = -483.05
Fit based upon off diagonal values = 0.97
Measures of factor score adequacy
MR1 MR2
Correlation of scores with factors 0.95 0.88
Multiple R square of scores with factors 0.91 0.78
Minimum correlation of possible factor scores 0.82 0.56
Coefficients and bootstrapped confidence intervals
low MR1 upper low MR2 upper
angry -23.81 0.61 26.05 -24.19 0.08 25.71
choices -23.82 0.31 25.50 -23.86 0.29 25.47
conscious -22.37 0.28 23.97 -22.33 0.32 23.99
depressed -29.25 0.67 31.54 -29.77 -0.07 31.05
depth -14.31 0.03 15.34 -13.97 0.52 15.65
disrespected -29.28 0.66 31.67 -29.75 0.01 31.23
embarrassed -30.00 0.45 31.95 -30.22 0.14 31.74
fear -32.64 0.54 34.84 -32.99 0.09 34.55
guilt -23.16 0.16 24.63 -22.95 0.46 24.78
happy -37.43 0.73 40.05 -38.02 -0.05 39.52
hungry -32.55 0.68 35.15 -32.98 0.10 34.79
love -26.76 0.71 28.98 -27.41 -0.15 28.45
nauseated -30.50 0.66 32.89 -30.99 0.01 32.46
odors -30.00 0.57 32.19 -30.37 0.06 31.87
pain -15.59 0.47 17.32 -15.88 0.09 17.08
pride -35.24 0.72 37.78 -35.80 -0.05 37.29
reasoning -28.37 0.16 29.94 -28.15 0.47 30.11
remembering -20.10 -0.03 21.12 -19.70 0.57 21.51
temperature -29.51 -0.03 30.86 -29.04 0.70 31.26
tired -35.66 0.51 38.10 -35.81 0.30 37.97
Interfactor correlations and bootstrapped confidence intervals
lower estimate upper
MR1-MR2 0.34 0.6 0.65Factor loadings table
Big factor loadings table for all studies (Studies 1-4)
Joining, by = "capacity"
Joining, by = "capacity"
Joining, by = c("order1_manual", "capacity")Figures
3D scatterplots
Factor loadings for the 40 mental capacities on the three rotated factors in Study 1. Items are colored by their dominant factor loading: Items that loaded most strongly on the body factor (bodily states and will) are in red; items that loaded most strongly on the heart factor (social-emotional experiences and morality) are in blue; and items that loaded most strongly on the mind factor (perceptual-cognitive abilities and goal pursuit) are in green.
Study 1 (adults)
Joining, by = "item"Study 2 (7-9yo)
Joining, by = "item"Study 3 (7-9yo)
Joining, by = "item"Study 4 (4-6yo)
Heatmaps
NOTE: set to 3 factors manually, for now.
Study 1 (adults)
Joining, by = "item"
Study 2 (7-9y)
Joining, by = "item"
Study 3 (7-9y)
Study 4 (4-6y)
Congruence
MR1 MR2
MR1 0.77 0.13
MR3 0.73 0.16
MR2 0.23 0.87
MR1 MR3 MR2
MR1 0.60 0.65 0.21
MR3 0.66 0.51 0.24
MR2 0.10 0.26 0.86Mean ratings
Studies 1-2
Mean ratings of 40 mental capacities for the 2 entities included in Studies 1-2. Participants responded on a 3-point scale (0 = “no”, 0.5 = “kinda”, 1 = “yes”). Error bars are nonparametric bootstrapped 95% confidence intervals. Mental capacities are grouped according to their dominant factor loading in Study 1 (adults).
Studies 3-4
Mean ratings of 20 mental capacities for the 9 entities included in Studies 3-4. Participants responded on a 3-point scale (0 = “no”, 0.5 = “kinda”, 1 = “yes”). Error bars are nonparametric bootstrapped 95% confidence intervals. Mental capacities are grouped according to their dominant factor loading in Study 3 (7-9y).
Mean factor scores
Studies 1-2
Joining, by = c("subid", "MR1", "MR2", "MR3")
Joining, by = c("age_group", "subid", "age", "character")
Column `character` joining factors with different levels, coercing to character vectorJoining, by = "subid"
Study 3
Studies 3-4
Factor scores by age
Study 1
Joining, by = "subid"
Study 2
Joining, by = "subid"
Studies 1-2
Joining, by = c("subid", "age", "character")
Column `character` joining factors with different levels, coercing to character vectorIgnoring unknown aesthetics: y
Studies 3-4
Joining, by = c("subid", "MR1", "MR3", "MR2")
Joining, by = c("age_group", "subid", "age", "character")
Column `character` joining factors with different levels, coercing to character vectorJoining, by = "subid"
BODY HEART MIND
BODY 1.0000000 0.5421433 0.4496306
HEART 0.5421433 1.0000000 0.4007229
MIND 0.4496306 0.4007229 1.0000000
BODY HEART MIND
BODY 1.0000000 0.6991161 0.6053140
HEART 0.6991161 1.0000000 0.6392113
MIND 0.6053140 0.6392113 1.0000000Linear mixed model fit by REML ['lmerMod']
Formula: HEART ~ BODY * scale(age, scale = F) + (1 | character)
Data: scores_s34_lm
REML criterion at convergence: 230
Scaled residuals:
Min 1Q Median 3Q Max
-2.3760 -0.5772 -0.2183 0.7973 2.3606
Random effects:
Groups Name Variance Std.Dev.
character (Intercept) 0.02187 0.1479
Residual 0.38421 0.6198
Number of obs: 115, groups: character, 9
Fixed effects:
Estimate Std. Error t value
(Intercept) 0.059349 0.077177 0.769
BODY 0.730164 0.072043 10.135
scale(age, scale = F) -0.163135 0.089169 -1.830
BODY:scale(age, scale = F) 0.003734 0.089871 0.042
Correlation of Fixed Effects:
(Intr) BODY s(,s=F
BODY 0.163
scl(g,sc=F) -0.027 -0.084
BODY:(,s=F) -0.048 0.032 0.233
Linear mixed model fit by REML ['lmerMod']
Formula: MIND ~ BODY * scale(age, scale = F) + (1 | character)
Data: scores_s34_lm
REML criterion at convergence: 270.2
Scaled residuals:
Min 1Q Median 3Q Max
-2.2049 -0.6245 -0.1414 0.5812 2.2751
Random effects:
Groups Name Variance Std.Dev.
character (Intercept) 0.01887 0.1374
Residual 0.55851 0.7473
Number of obs: 115, groups: character, 9
Fixed effects:
Estimate Std. Error t value
(Intercept) -0.332875 0.084851 -3.923
BODY 0.662356 0.084028 7.883
scale(age, scale = F) 0.005836 0.106831 0.055
BODY:scale(age, scale = F) -0.033039 0.108062 -0.306
Correlation of Fixed Effects:
(Intr) BODY s(,s=F
BODY 0.172
scl(g,sc=F) -0.028 -0.078
BODY:(,s=F) -0.053 0.027 0.234
Linear mixed model fit by REML ['lmerMod']
Formula: HEART ~ MIND * scale(age, scale = F) + (1 | character)
Data: scores_s34_lm
REML criterion at convergence: 240.8
Scaled residuals:
Min 1Q Median 3Q Max
-2.1222 -0.6935 -0.1318 0.6780 2.1731
Random effects:
Groups Name Variance Std.Dev.
character (Intercept) 0.06609 0.2571
Residual 0.40525 0.6366
Number of obs: 115, groups: character, 9
Fixed effects:
Estimate Std. Error t value
(Intercept) 0.20037 0.10869 1.844
MIND 0.59260 0.06617 8.955
scale(age, scale = F) -0.13866 0.09544 -1.453
MIND:scale(age, scale = F) -0.08754 0.09148 -0.957
Correlation of Fixed Effects:
(Intr) MIND s(,s=F
MIND 0.278
scl(g,sc=F) -0.031 -0.060
MIND:(,s=F) -0.049 -0.033 0.335Joining, by = c("subid", "study", "character")
Column `study` joining factors with different levels, coercing to character vectorColumn `character` joining factors with different levels, coercing to character vectorJoining, by = "subid"
Joining, by = "capacity"
Joining, by = "capacity"
Joining, by = c("subid", "study")
Column `study` joining factors with different levels, coercing to character vectorJoining, by = "subid"
Joining, by = "capacity"
Joining, by = c("subid", "capacity", "response", "age_group")
Factor Analysis using method = minres
Call: fa(r = ., nfactors = 6, rotate = "none", fm = "minres")
Standardized loadings (pattern matrix) based upon correlation matrix
MR1 MR2 MR3 MR4 MR5 MR6 h2 u2 com
fear 0.79 -0.16 -0.18 0.14 -0.06 0.09 0.71 0.29 1.3
angry 0.79 -0.16 0.03 -0.10 -0.14 0.10 0.69 0.31 1.2
tired 0.75 0.07 0.00 0.09 0.16 -0.12 0.62 0.38 1.2
depressed 0.75 -0.23 0.18 -0.17 0.02 0.05 0.67 0.33 1.4
hungry 0.74 -0.03 -0.50 0.12 -0.07 0.07 0.83 0.17 1.8
love 0.70 -0.16 -0.02 0.16 0.13 -0.09 0.56 0.44 1.3
happy 0.70 -0.19 -0.02 -0.28 -0.08 -0.29 0.69 0.31 1.9
pain 0.70 -0.17 -0.22 -0.13 0.02 -0.07 0.58 0.42 1.4
pride 0.69 -0.21 0.23 -0.14 -0.20 -0.15 0.65 0.35 1.8
odors 0.62 -0.01 -0.37 0.16 -0.01 0.04 0.55 0.45 1.8
disrespected 0.58 -0.22 0.23 -0.12 0.19 0.17 0.52 0.48 2.3
nauseated 0.52 0.11 -0.25 -0.07 0.04 0.26 0.42 0.58 2.1
conscious 0.50 0.17 0.06 0.11 0.30 -0.16 0.42 0.58 2.3
embarrassed 0.50 -0.10 0.40 0.07 0.04 0.22 0.47 0.53 2.5
reasoning 0.44 0.64 0.00 0.08 0.02 -0.21 0.65 0.35 2.1
remembering 0.37 0.59 0.14 0.15 -0.31 0.06 0.63 0.37 2.7
choices 0.49 0.50 0.11 -0.09 -0.09 -0.05 0.52 0.48 2.2
temperature 0.39 0.47 0.11 -0.21 -0.06 0.16 0.46 0.54 2.9
depth 0.31 0.43 0.11 -0.06 0.31 0.09 0.40 0.60 3.0
guilt 0.42 -0.28 0.43 0.41 -0.10 -0.03 0.62 0.38 3.8
MR1 MR2 MR3 MR4 MR5 MR6
SS loadings 7.35 1.82 1.06 0.54 0.47 0.41
Proportion Var 0.37 0.09 0.05 0.03 0.02 0.02
Cumulative Var 0.37 0.46 0.51 0.54 0.56 0.58
Proportion Explained 0.63 0.16 0.09 0.05 0.04 0.04
Cumulative Proportion 0.63 0.79 0.88 0.92 0.96 1.00
Mean item complexity = 2.1
Test of the hypothesis that 6 factors are sufficient.
The degrees of freedom for the null model are 190 and the objective function was 10.74 with Chi Square of 1229.69
The degrees of freedom for the model are 85 and the objective function was 0.98
The root mean square of the residuals (RMSR) is 0.03
The df corrected root mean square of the residuals is 0.04
The harmonic number of observations is 123 with the empirical chi square 39.19 with prob < 1
The total number of observations was 123 with Likelihood Chi Square = 108.38 with prob < 0.044
Tucker Lewis Index of factoring reliability = 0.948
RMSEA index = 0.058 and the 90 % confidence intervals are 0.008 0.072
BIC = -300.66
Fit based upon off diagonal values = 0.99
Measures of factor score adequacy
MR1 MR2 MR3 MR4 MR5
Correlation of scores with factors 0.98 0.90 0.88 0.77 0.72
Multiple R square of scores with factors 0.95 0.82 0.77 0.59 0.51
Minimum correlation of possible factor scores 0.91 0.63 0.54 0.18 0.03
MR6
Correlation of scores with factors 0.71
Multiple R square of scores with factors 0.50
Minimum correlation of possible factor scores 0.00
Factor Analysis using method = minres
Call: fa(r = ., nfactors = 3, rotate = "oblimin", fm = "minres")
Standardized loadings (pattern matrix) based upon correlation matrix
MR1 MR3 MR2 h2 u2 com
depressed 0.73 0.13 0.01 0.66 0.34 1.1
pride 0.70 0.06 0.04 0.56 0.44 1.0
embarrassed 0.68 -0.21 0.13 0.40 0.60 1.3
disrespected 0.67 0.00 -0.01 0.44 0.56 1.0
guilt 0.66 -0.16 -0.06 0.33 0.67 1.1
angry 0.57 0.31 0.05 0.65 0.35 1.6
happy 0.48 0.31 0.00 0.50 0.50 1.7
love 0.45 0.34 0.02 0.51 0.49 1.9
tired 0.37 0.31 0.28 0.57 0.43 2.8
hungry -0.05 0.92 0.04 0.82 0.18 1.0
odors -0.01 0.71 0.06 0.54 0.46 1.0
fear 0.35 0.58 0.00 0.68 0.32 1.6
pain 0.29 0.55 -0.04 0.55 0.45 1.5
nauseated 0.02 0.47 0.18 0.33 0.67 1.3
reasoning -0.12 0.08 0.77 0.60 0.40 1.1
choices 0.09 -0.01 0.69 0.52 0.48 1.0
remembering -0.01 -0.05 0.68 0.44 0.56 1.0
temperature 0.07 -0.04 0.60 0.37 0.63 1.0
depth 0.04 -0.06 0.53 0.27 0.73 1.0
conscious 0.24 0.12 0.31 0.28 0.72 2.2
MR1 MR3 MR2
SS loadings 4.12 3.28 2.61
Proportion Var 0.21 0.16 0.13
Cumulative Var 0.21 0.37 0.50
Proportion Explained 0.41 0.33 0.26
Cumulative Proportion 0.41 0.74 1.00
With factor correlations of
MR1 MR3 MR2
MR1 1.00 0.56 0.32
MR3 0.56 1.00 0.38
MR2 0.32 0.38 1.00
Mean item complexity = 1.4
Test of the hypothesis that 3 factors are sufficient.
The degrees of freedom for the null model are 190 and the objective function was 10.74 with Chi Square of 1229.69
The degrees of freedom for the model are 133 and the objective function was 1.67
The root mean square of the residuals (RMSR) is 0.05
The df corrected root mean square of the residuals is 0.06
The harmonic number of observations is 123 with the empirical chi square 99.6 with prob < 0.99
The total number of observations was 123 with Likelihood Chi Square = 187.7 with prob < 0.0013
Tucker Lewis Index of factoring reliability = 0.923
RMSEA index = 0.066 and the 90 % confidence intervals are 0.037 0.077
BIC = -452.32
Fit based upon off diagonal values = 0.98
Measures of factor score adequacy
MR1 MR3 MR2
Correlation of scores with factors 0.94 0.95 0.91
Multiple R square of scores with factors 0.89 0.91 0.83
Minimum correlation of possible factor scores 0.78 0.82 0.66An ultra-Heywood case was detected. Examine the results carefullyFactor Analysis using method = minres
Call: fa(r = ., nfactors = 6, rotate = "none", fm = "minres")
Standardized loadings (pattern matrix) based upon correlation matrix
MR1 MR2 MR3 MR4 MR5 MR6 h2 u2 com
tired 0.75 0.05 -0.06 0.03 -0.24 0.00 0.62 0.3790 1.2
hungry 0.74 -0.20 -0.22 -0.05 -0.23 0.08 0.70 0.3033 1.6
disrespected 0.68 -0.06 0.05 -0.29 0.02 -0.19 0.59 0.4118 1.6
angry 0.67 -0.02 0.01 -0.35 -0.17 -0.19 0.64 0.3584 1.8
pride 0.65 -0.15 0.15 -0.07 0.25 0.09 0.54 0.4570 1.6
happy 0.64 -0.24 0.28 0.25 0.18 -0.02 0.64 0.3569 2.2
nauseated 0.63 -0.21 -0.14 0.01 -0.02 -0.07 0.47 0.5333 1.4
reasoning 0.61 0.47 -0.39 -0.21 0.42 0.18 1.00 -0.0016 4.1
fear 0.59 -0.19 0.01 0.16 -0.05 0.24 0.47 0.5262 1.8
depressed 0.59 -0.12 0.09 -0.14 0.08 -0.25 0.46 0.5429 1.6
odors 0.57 -0.21 -0.18 -0.13 -0.08 0.24 0.49 0.5083 2.1
embarrassed 0.56 -0.10 -0.06 0.16 -0.01 0.05 0.35 0.6513 1.3
guilt 0.55 0.16 -0.15 0.12 0.14 -0.15 0.41 0.5911 1.7
choices 0.54 0.05 -0.01 0.14 0.08 -0.24 0.38 0.6211 1.6
love 0.54 -0.29 0.23 0.10 0.25 0.11 0.51 0.4894 2.7
pain 0.53 -0.02 0.04 -0.07 -0.16 0.06 0.31 0.6877 1.3
temperature 0.50 0.31 -0.01 0.32 -0.03 -0.18 0.49 0.5123 2.8
depth 0.45 0.30 0.00 0.17 -0.12 -0.10 0.35 0.6532 2.4
remembering 0.45 0.19 -0.19 0.25 -0.15 0.15 0.38 0.6221 3.0
conscious 0.55 0.52 0.59 -0.15 -0.12 0.21 1.00 -0.0028 3.5
MR1 MR2 MR3 MR4 MR5 MR6
SS loadings 7.09 1.11 0.83 0.67 0.59 0.51
Proportion Var 0.35 0.06 0.04 0.03 0.03 0.03
Cumulative Var 0.35 0.41 0.45 0.49 0.51 0.54
Proportion Explained 0.66 0.10 0.08 0.06 0.05 0.05
Cumulative Proportion 0.66 0.76 0.84 0.90 0.95 1.00
Mean item complexity = 2.1
Test of the hypothesis that 6 factors are sufficient.
The degrees of freedom for the null model are 190 and the objective function was 8.67 with Chi Square of 984.43
The degrees of freedom for the model are 85 and the objective function was 0.84
The root mean square of the residuals (RMSR) is 0.03
The df corrected root mean square of the residuals is 0.05
The harmonic number of observations is 120 with the empirical chi square 45.72 with prob < 1
The total number of observations was 122 with Likelihood Chi Square = 91.71 with prob < 0.29
Tucker Lewis Index of factoring reliability = 0.98
RMSEA index = 0.04 and the 90 % confidence intervals are 0 0.058
BIC = -316.63
Fit based upon off diagonal values = 0.99
Measures of factor score adequacy
MR1 MR2 MR3 MR4 MR5
Correlation of scores with factors 0.97 0.93 0.97 0.79 0.86
Multiple R square of scores with factors 0.95 0.86 0.94 0.62 0.74
Minimum correlation of possible factor scores 0.90 0.72 0.88 0.25 0.47
MR6
Correlation of scores with factors 0.76
Multiple R square of scores with factors 0.57
Minimum correlation of possible factor scores 0.15
Factor Analysis using method = minres
Call: fa(r = ., nfactors = 2, rotate = "oblimin", fm = "minres")
Standardized loadings (pattern matrix) based upon correlation matrix
MR1 MR2 h2 u2 com
love 0.71 -0.18 0.37 0.63 1.1
pride 0.70 -0.03 0.46 0.54 1.0
hungry 0.69 0.09 0.56 0.44 1.0
happy 0.68 -0.02 0.44 0.56 1.0
nauseated 0.65 0.01 0.43 0.57 1.0
odors 0.64 -0.06 0.37 0.63 1.0
fear 0.62 -0.01 0.38 0.62 1.0
disrespected 0.60 0.11 0.46 0.54 1.1
depressed 0.55 0.07 0.36 0.64 1.0
angry 0.53 0.18 0.44 0.56 1.2
embarrassed 0.47 0.13 0.32 0.68 1.2
tired 0.44 0.40 0.57 0.43 2.0
pain 0.42 0.15 0.28 0.72 1.3
temperature -0.05 0.68 0.42 0.58 1.0
depth -0.06 0.63 0.36 0.64 1.0
reasoning 0.15 0.51 0.38 0.62 1.2
remembering 0.07 0.47 0.26 0.74 1.0
guilt 0.19 0.46 0.35 0.65 1.3
conscious 0.14 0.45 0.31 0.69 1.2
choices 0.29 0.32 0.30 0.70 2.0
MR1 MR2
SS loadings 5.26 2.56
Proportion Var 0.26 0.13
Cumulative Var 0.26 0.39
Proportion Explained 0.67 0.33
Cumulative Proportion 0.67 1.00
With factor correlations of
MR1 MR2
MR1 1.00 0.63
MR2 0.63 1.00
Mean item complexity = 1.2
Test of the hypothesis that 2 factors are sufficient.
The degrees of freedom for the null model are 190 and the objective function was 8.67 with Chi Square of 984.43
The degrees of freedom for the model are 151 and the objective function was 2.02
The root mean square of the residuals (RMSR) is 0.06
The df corrected root mean square of the residuals is 0.07
The harmonic number of observations is 120 with the empirical chi square 160.35 with prob < 0.29
The total number of observations was 122 with Likelihood Chi Square = 226.65 with prob < 6.6e-05
Tucker Lewis Index of factoring reliability = 0.878
RMSEA index = 0.072 and the 90 % confidence intervals are 0.046 0.081
BIC = -498.76
Fit based upon off diagonal values = 0.97
Measures of factor score adequacy
MR1 MR2
Correlation of scores with factors 0.95 0.90
Multiple R square of scores with factors 0.90 0.81
Minimum correlation of possible factor scores 0.80 0.61Matrix was not positive definite, smoothing was doneMatrix was not positive definite, smoothing was doneMatrix was not positive definite, smoothing was doneMatrix was not positive definite, smoothing was doneThe estimated weights for the factor scores are probably incorrect. Try a different factor extraction method.Factor Analysis using method = minres
Call: fa(r = ., nfactors = 6, rotate = "none", fm = "minres")
Standardized loadings (pattern matrix) based upon correlation matrix
MR1 MR2 MR3 MR4 MR5 MR6 h2 u2 com
angry 0.89 -0.10 -0.05 0.07 -0.14 -0.33 0.93 0.072 1.4
fear 0.86 -0.16 -0.28 0.00 0.18 0.01 0.87 0.132 1.4
tired 0.82 0.08 -0.11 -0.16 0.09 0.17 0.75 0.248 1.2
pride 0.81 -0.28 0.23 -0.28 -0.10 -0.19 0.91 0.088 1.9
depressed 0.80 -0.27 0.15 -0.03 -0.32 0.00 0.84 0.162 1.7
pain 0.80 -0.19 -0.29 -0.09 0.06 0.00 0.76 0.235 1.4
hungry 0.77 -0.02 -0.55 0.07 0.17 -0.04 0.92 0.075 2.0
happy 0.74 -0.18 -0.08 -0.26 -0.18 -0.09 0.70 0.302 1.6
love 0.71 -0.29 0.07 -0.05 0.18 0.17 0.66 0.338 1.6
disrespected 0.69 -0.29 0.31 0.09 -0.16 0.23 0.74 0.263 2.3
odors 0.66 0.01 -0.42 0.10 0.14 -0.07 0.65 0.355 1.9
choices 0.64 0.56 0.15 -0.05 -0.05 -0.06 0.75 0.254 2.1
guilt 0.59 -0.38 0.54 0.23 0.29 -0.03 0.92 0.079 3.6
conscious 0.57 0.34 0.06 -0.10 0.05 0.12 0.47 0.533 1.9
embarrassed 0.53 -0.23 0.50 0.12 0.09 0.08 0.62 0.383 2.6
remembering 0.43 0.70 0.31 0.23 0.14 -0.29 0.93 0.069 3.0
reasoning 0.48 0.68 0.09 -0.11 0.11 0.09 0.74 0.265 2.0
temperature 0.44 0.55 0.04 -0.01 -0.20 -0.03 0.54 0.461 2.2
depth 0.40 0.51 0.05 -0.12 -0.06 0.29 0.52 0.478 2.8
nauseated 0.55 0.06 -0.27 0.58 -0.28 0.16 0.82 0.184 3.1
MR1 MR2 MR3 MR4 MR5 MR6
SS loadings 9.11 2.56 1.58 0.71 0.57 0.50
Proportion Var 0.46 0.13 0.08 0.04 0.03 0.02
Cumulative Var 0.46 0.58 0.66 0.70 0.73 0.75
Proportion Explained 0.61 0.17 0.11 0.05 0.04 0.03
Cumulative Proportion 0.61 0.78 0.88 0.93 0.97 1.00
Mean item complexity = 2.1
Test of the hypothesis that 6 factors are sufficient.
The degrees of freedom for the null model are 190 and the objective function was 40.61 with Chi Square of 4649.56
The degrees of freedom for the model are 85 and the objective function was 23.43
The root mean square of the residuals (RMSR) is 0.03
The df corrected root mean square of the residuals is 0.05
The harmonic number of observations is 79 with the empirical chi square 29.46 with prob < 1
The total number of observations was 123 with Likelihood Chi Square = 2589.13 with prob < 0
Tucker Lewis Index of factoring reliability = -0.303
RMSEA index = 0.517 and the 90 % confidence intervals are 0.475 NA
BIC = 2180.09
Fit based upon off diagonal values = 1Matrix was not positive definite, smoothing was doneMatrix was not positive definite, smoothing was doneMatrix was not positive definite, smoothing was done A loading greater than abs(1) was detected. Examine the loadings carefully.Matrix was not positive definite, smoothing was doneThe estimated weights for the factor scores are probably incorrect. Try a different factor extraction method.Factor Analysis using method = minres
Call: fa(r = ., nfactors = 3, rotate = "oblimin", fm = "minres")
Warning: A Heywood case was detected.
Standardized loadings (pattern matrix) based upon correlation matrix
MR1 MR3 MR2 h2 u2 com
hungry 1.00 -0.16 0.03 0.90 0.097 1.1
odors 0.80 -0.10 0.08 0.62 0.383 1.1
fear 0.80 0.21 0.00 0.84 0.158 1.1
pain 0.79 0.19 -0.05 0.77 0.233 1.1
tired 0.54 0.21 0.29 0.69 0.313 1.9
angry 0.54 0.40 0.15 0.78 0.223 2.0
happy 0.51 0.36 0.03 0.58 0.422 1.8
nauseated 0.47 0.03 0.14 0.31 0.693 1.2
guilt -0.10 0.88 -0.02 0.69 0.313 1.0
embarrassed -0.19 0.83 0.09 0.60 0.396 1.1
disrespected 0.11 0.74 0.02 0.65 0.352 1.0
pride 0.27 0.69 0.05 0.76 0.239 1.3
depressed 0.32 0.63 0.03 0.72 0.280 1.5
love 0.38 0.53 -0.05 0.59 0.406 1.8
reasoning 0.03 -0.10 0.86 0.72 0.283 1.0
remembering -0.18 0.06 0.84 0.65 0.349 1.1
choices 0.06 0.09 0.81 0.76 0.238 1.0
temperature 0.07 -0.07 0.69 0.50 0.504 1.0
depth 0.05 -0.06 0.63 0.40 0.597 1.0
conscious 0.17 0.11 0.53 0.44 0.557 1.3
MR1 MR3 MR2
SS loadings 5.09 4.21 3.66
Proportion Var 0.25 0.21 0.18
Cumulative Var 0.25 0.47 0.65
Proportion Explained 0.39 0.32 0.28
Cumulative Proportion 0.39 0.72 1.00
With factor correlations of
MR1 MR3 MR2
MR1 1.00 0.48 0.38
MR3 0.48 1.00 0.28
MR2 0.38 0.28 1.00
Mean item complexity = 1.3
Test of the hypothesis that 3 factors are sufficient.
The degrees of freedom for the null model are 190 and the objective function was 40.61 with Chi Square of 4649.56
The degrees of freedom for the model are 133 and the objective function was 25.65
The root mean square of the residuals (RMSR) is 0.05
The df corrected root mean square of the residuals is 0.06
The harmonic number of observations is 79 with the empirical chi square 80.47 with prob < 1
The total number of observations was 123 with Likelihood Chi Square = 2885.57 with prob < 0
Tucker Lewis Index of factoring reliability = 0.102
RMSEA index = 0.43 and the 90 % confidence intervals are 0.399 NA
BIC = 2245.55
Fit based upon off diagonal values = 0.99Factor Analysis using method = minres
Call: fa(r = ., nfactors = 6, rotate = "none", fm = "minres")
Standardized loadings (pattern matrix) based upon correlation matrix
MR1 MR2 MR3 MR4 MR5 MR6 h2 u2 com
hungry 0.76 -0.09 0.01 -0.39 0.02 -0.09 0.75 0.25 1.5
tired 0.74 0.06 -0.06 -0.13 -0.16 -0.22 0.64 0.36 1.4
happy 0.69 -0.22 0.31 0.27 0.01 -0.06 0.70 0.30 2.0
angry 0.69 -0.12 -0.45 -0.05 -0.10 0.02 0.71 0.29 1.9
disrespected 0.67 -0.19 -0.31 0.13 0.09 0.21 0.65 0.35 2.0
pride 0.67 -0.19 0.13 0.09 0.10 0.07 0.52 0.48 1.4
nauseated 0.65 -0.13 0.00 -0.06 0.08 0.05 0.46 0.54 1.1
odors 0.63 -0.09 0.01 -0.35 0.00 -0.05 0.53 0.47 1.6
fear 0.62 -0.07 0.24 -0.20 -0.03 -0.05 0.49 0.51 1.6
depressed 0.62 -0.23 -0.13 0.10 -0.33 0.09 0.58 0.42 2.1
love 0.60 -0.29 0.34 0.17 -0.09 -0.02 0.59 0.41 2.4
pain 0.57 -0.07 -0.18 0.06 0.34 -0.25 0.54 0.46 2.4
embarrassed 0.56 -0.01 0.13 0.03 0.27 0.02 0.40 0.60 1.6
conscious 0.56 0.14 -0.21 0.37 0.08 -0.07 0.52 0.48 2.3
choices 0.54 0.13 0.12 0.08 -0.23 0.17 0.42 0.58 1.9
reasoning 0.53 0.29 -0.12 -0.14 0.08 0.24 0.46 0.54 2.4
temperature 0.52 0.49 0.11 0.22 0.07 0.02 0.58 0.42 2.5
guilt 0.51 0.30 0.09 -0.07 0.03 0.31 0.46 0.54 2.5
depth 0.47 0.38 -0.11 0.13 -0.21 -0.30 0.53 0.47 3.5
remembering 0.42 0.40 0.16 -0.16 0.01 -0.06 0.40 0.60 2.6
MR1 MR2 MR3 MR4 MR5 MR6
SS loadings 7.35 1.08 0.79 0.73 0.48 0.47
Proportion Var 0.37 0.05 0.04 0.04 0.02 0.02
Cumulative Var 0.37 0.42 0.46 0.50 0.52 0.55
Proportion Explained 0.67 0.10 0.07 0.07 0.04 0.04
Cumulative Proportion 0.67 0.77 0.85 0.91 0.96 1.00
Mean item complexity = 2
Test of the hypothesis that 6 factors are sufficient.
The degrees of freedom for the null model are 190 and the objective function was 9.83 with Chi Square of 1115.79
The degrees of freedom for the model are 85 and the objective function was 1.3
The root mean square of the residuals (RMSR) is 0.04
The df corrected root mean square of the residuals is 0.05
The harmonic number of observations is 104 with the empirical chi square 51.93 with prob < 1
The total number of observations was 122 with Likelihood Chi Square = 142.11 with prob < 1e-04
Tucker Lewis Index of factoring reliability = 0.856
RMSEA index = 0.084 and the 90 % confidence intervals are 0.052 0.096
BIC = -266.23
Fit based upon off diagonal values = 0.99
Measures of factor score adequacy
MR1 MR2 MR3 MR4 MR5
Correlation of scores with factors 0.97 0.84 0.83 0.81 0.71
Multiple R square of scores with factors 0.95 0.70 0.68 0.66 0.51
Minimum correlation of possible factor scores 0.89 0.40 0.37 0.32 0.02
MR6
Correlation of scores with factors 0.71
Multiple R square of scores with factors 0.50
Minimum correlation of possible factor scores 0.01
Factor Analysis using method = minres
Call: fa(r = ., nfactors = 2, rotate = "oblimin", fm = "minres")
Standardized loadings (pattern matrix) based upon correlation matrix
MR1 MR2 h2 u2 com
happy 0.73 -0.04 0.50 0.50 1.0
pride 0.72 -0.05 0.48 0.52 1.0
love 0.71 -0.14 0.41 0.59 1.1
depressed 0.70 -0.09 0.42 0.58 1.0
disrespected 0.69 -0.02 0.46 0.54 1.0
hungry 0.68 0.11 0.57 0.43 1.1
nauseated 0.67 0.01 0.45 0.55 1.0
angry 0.65 0.05 0.46 0.54 1.0
odors 0.59 0.06 0.40 0.60 1.0
fear 0.56 0.10 0.39 0.61 1.1
tired 0.55 0.26 0.54 0.46 1.4
pain 0.51 0.07 0.31 0.69 1.0
embarrassed 0.46 0.15 0.31 0.69 1.2
conscious 0.34 0.29 0.31 0.69 2.0
choices 0.31 0.31 0.31 0.69 2.0
temperature -0.03 0.73 0.50 0.50 1.0
remembering -0.04 0.61 0.34 0.66 1.0
depth 0.07 0.51 0.31 0.69 1.0
guilt 0.13 0.49 0.34 0.66 1.1
reasoning 0.17 0.47 0.35 0.65 1.3
MR1 MR2
SS loadings 5.89 2.29
Proportion Var 0.29 0.11
Cumulative Var 0.29 0.41
Proportion Explained 0.72 0.28
Cumulative Proportion 0.72 1.00
With factor correlations of
MR1 MR2
MR1 1.00 0.59
MR2 0.59 1.00
Mean item complexity = 1.2
Test of the hypothesis that 2 factors are sufficient.
The degrees of freedom for the null model are 190 and the objective function was 9.83 with Chi Square of 1115.79
The degrees of freedom for the model are 151 and the objective function was 2.72
The root mean square of the residuals (RMSR) is 0.07
The df corrected root mean square of the residuals is 0.08
The harmonic number of observations is 104 with the empirical chi square 177.48 with prob < 0.069
The total number of observations was 122 with Likelihood Chi Square = 305.58 with prob < 1.5e-12
Tucker Lewis Index of factoring reliability = 0.787
RMSEA index = 0.099 and the 90 % confidence intervals are 0.077 0.107
BIC = -419.83
Fit based upon off diagonal values = 0.97
Measures of factor score adequacy
MR1 MR2
Correlation of scores with factors 0.96 0.89
Multiple R square of scores with factors 0.92 0.79
Minimum correlation of possible factor scores 0.83 0.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